diff --git a/mediapipe/tasks/web/vision/BUILD b/mediapipe/tasks/web/vision/BUILD
index 67db27ddb..1f28cb0fe 100644
--- a/mediapipe/tasks/web/vision/BUILD
+++ b/mediapipe/tasks/web/vision/BUILD
@@ -19,6 +19,7 @@ mediapipe_files(srcs = [
 
 VISION_LIBS = [
     "//mediapipe/tasks/web/core:fileset_resolver",
+    "//mediapipe/tasks/web/vision/face_landmarker",
     "//mediapipe/tasks/web/vision/face_stylizer",
     "//mediapipe/tasks/web/vision/gesture_recognizer",
     "//mediapipe/tasks/web/vision/hand_landmarker",
diff --git a/mediapipe/tasks/web/vision/README.md b/mediapipe/tasks/web/vision/README.md
index a1444e10b..ebeac54c5 100644
--- a/mediapipe/tasks/web/vision/README.md
+++ b/mediapipe/tasks/web/vision/README.md
@@ -2,6 +2,23 @@
 
 This package contains the vision tasks for MediaPipe.
 
+## Face Landmark Detection
+
+The MediaPipe Face Landmarker task lets you detect the landmarks of faces in
+an image. You can use this Task to localize key points of a face and render
+visual effects over the faces.
+
+```
+const vision = await FilesetResolver.forVisionTasks(
+    "https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@latest/wasm"
+);
+const faceLandmarker = await FaceLandmarker.createFromModelPath(vision,
+    "model.task"
+);
+const image = document.getElementById("image") as HTMLImageElement;
+const landmarks = faceLandmarker.detect(image);
+```
+
 ## Face Stylizer
 
 The MediaPipe Face Stylizer lets you perform face stylization on images.
diff --git a/mediapipe/tasks/web/vision/face_landmarker/BUILD b/mediapipe/tasks/web/vision/face_landmarker/BUILD
new file mode 100644
index 000000000..bd5e971a3
--- /dev/null
+++ b/mediapipe/tasks/web/vision/face_landmarker/BUILD
@@ -0,0 +1,81 @@
+# This contains the MediaPipe Face Landmarker Task.
+#
+# This task takes video frames and outputs synchronized frames along with
+# the detection results for one or more face categories, using Face Landmarker.
+
+load("//mediapipe/framework/port:build_config.bzl", "mediapipe_ts_declaration", "mediapipe_ts_library")
+load("@npm//@bazel/jasmine:index.bzl", "jasmine_node_test")
+
+package(default_visibility = ["//mediapipe/tasks:internal"])
+
+licenses(["notice"])
+
+mediapipe_ts_library(
+    name = "face_landmarker",
+    srcs = ["face_landmarker.ts"],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":face_landmarker_types",
+        "//mediapipe/framework:calculator_jspb_proto",
+        "//mediapipe/framework:calculator_options_jspb_proto",
+        "//mediapipe/framework/formats:classification_jspb_proto",
+        "//mediapipe/framework/formats:landmark_jspb_proto",
+        "//mediapipe/tasks/cc/core/proto:base_options_jspb_proto",
+        "//mediapipe/tasks/cc/vision/face_detector/proto:face_detector_graph_options_jspb_proto",
+        "//mediapipe/tasks/cc/vision/face_geometry/proto:face_geometry_jspb_proto",
+        "//mediapipe/tasks/cc/vision/face_landmarker/proto:face_landmarker_graph_options_jspb_proto",
+        "//mediapipe/tasks/cc/vision/face_landmarker/proto:face_landmarks_detector_graph_options_jspb_proto",
+        "//mediapipe/tasks/web/components/containers:category",
+        "//mediapipe/tasks/web/components/containers:classification_result",
+        "//mediapipe/tasks/web/components/containers:landmark",
+        "//mediapipe/tasks/web/components/containers:matrix",
+        "//mediapipe/tasks/web/components/processors:classifier_result",
+        "//mediapipe/tasks/web/core",
+        "//mediapipe/tasks/web/vision/core:image_processing_options",
+        "//mediapipe/tasks/web/vision/core:vision_task_runner",
+        "//mediapipe/web/graph_runner:graph_runner_ts",
+    ],
+)
+
+mediapipe_ts_declaration(
+    name = "face_landmarker_types",
+    srcs = [
+        "face_landmarker_options.d.ts",
+        "face_landmarker_result.d.ts",
+    ],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/tasks/web/components/containers:category",
+        "//mediapipe/tasks/web/components/containers:classification_result",
+        "//mediapipe/tasks/web/components/containers:landmark",
+        "//mediapipe/tasks/web/components/containers:matrix",
+        "//mediapipe/tasks/web/core",
+        "//mediapipe/tasks/web/vision/core:vision_task_options",
+    ],
+)
+
+mediapipe_ts_library(
+    name = "face_landmarker_test_lib",
+    testonly = True,
+    srcs = [
+        "face_landmarker_test.ts",
+    ],
+    deps = [
+        ":face_landmarker",
+        ":face_landmarker_types",
+        "//mediapipe/framework:calculator_jspb_proto",
+        "//mediapipe/framework/formats:classification_jspb_proto",
+        "//mediapipe/framework/formats:landmark_jspb_proto",
+        "//mediapipe/framework/formats:matrix_data_jspb_proto",
+        "//mediapipe/tasks/cc/vision/face_geometry/proto:face_geometry_jspb_proto",
+        "//mediapipe/tasks/web/core",
+        "//mediapipe/tasks/web/core:task_runner_test_utils",
+        "//mediapipe/tasks/web/vision/core:vision_task_runner",
+    ],
+)
+
+jasmine_node_test(
+    name = "face_landmarker_test",
+    tags = ["nomsan"],
+    deps = [":face_landmarker_test_lib"],
+)
diff --git a/mediapipe/tasks/web/vision/face_landmarker/face_landmarker.ts b/mediapipe/tasks/web/vision/face_landmarker/face_landmarker.ts
new file mode 100644
index 000000000..113dd37c5
--- /dev/null
+++ b/mediapipe/tasks/web/vision/face_landmarker/face_landmarker.ts
@@ -0,0 +1,355 @@
+/**
+ * Copyright 2023 The MediaPipe Authors. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+import {CalculatorGraphConfig} from '../../../../framework/calculator_pb';
+import {CalculatorOptions} from '../../../../framework/calculator_options_pb';
+import {ClassificationList as ClassificationListProto} from '../../../../framework/formats/classification_pb';
+import {NormalizedLandmarkList as NormalizedLandmarkListProto} from '../../../../framework/formats/landmark_pb';
+import {BaseOptions as BaseOptionsProto} from '../../../../tasks/cc/core/proto/base_options_pb';
+import {FaceDetectorGraphOptions} from '../../../../tasks/cc/vision/face_detector/proto/face_detector_graph_options_pb';
+import {FaceGeometry as FaceGeometryProto} from '../../../../tasks/cc/vision/face_geometry/proto/face_geometry_pb';
+import {FaceLandmarkerGraphOptions} from '../../../../tasks/cc/vision/face_landmarker/proto/face_landmarker_graph_options_pb';
+import {FaceLandmarksDetectorGraphOptions} from '../../../../tasks/cc/vision/face_landmarker/proto/face_landmarks_detector_graph_options_pb';
+import {NormalizedLandmark} from '../../../../tasks/web/components/containers/landmark';
+import {convertFromClassifications} from '../../../../tasks/web/components/processors/classifier_result';
+import {WasmFileset} from '../../../../tasks/web/core/wasm_fileset';
+import {ImageProcessingOptions} from '../../../../tasks/web/vision/core/image_processing_options';
+import {VisionGraphRunner, VisionTaskRunner} from '../../../../tasks/web/vision/core/vision_task_runner';
+import {ImageSource, WasmModule} from '../../../../web/graph_runner/graph_runner';
+// Placeholder for internal dependency on trusted resource url
+
+import {FaceLandmarkerOptions} from './face_landmarker_options';
+import {FaceLandmarkerResult} from './face_landmarker_result';
+
+export * from './face_landmarker_options';
+export * from './face_landmarker_result';
+export {ImageSource};
+
+// The OSS JS API does not support the builder pattern.
+// tslint:disable:jspb-use-builder-pattern
+
+const IMAGE_STREAM = 'image_in';
+const NORM_RECT_STREAM = 'norm_rect';
+const LANDMARKS_STREAM = 'face_landmarks';
+const BLENDSHAPES_STREAM = 'blendshapes';
+const FACE_GEOMETRY_STREAM = 'face_geometry';
+const FACE_LANDMARKER_GRAPH =
+    'mediapipe.tasks.vision.face_landmarker.FaceLandmarkerGraph';
+
+const DEFAULT_NUM_FACES = 1;
+const DEFAULT_SCORE_THRESHOLD = 0.5;
+
+/**
+ * Performs face landmarks detection on images.
+ *
+ * This API expects a pre-trained face landmarker model asset bundle.
+ */
+export class FaceLandmarker extends VisionTaskRunner {
+  private result: FaceLandmarkerResult = {faceLandmarks: []};
+  private outputFaceBlendshapes = false;
+  private outputFacialTransformationMatrixes = false;
+
+  private readonly options: FaceLandmarkerGraphOptions;
+  private readonly faceLandmarksDetectorGraphOptions:
+      FaceLandmarksDetectorGraphOptions;
+  private readonly faceDetectorGraphOptions: FaceDetectorGraphOptions;
+
+  /**
+   * Initializes the Wasm runtime and creates a new `FaceLandmarker` from the
+   * provided options.
+   * @param wasmFileset A configuration object that provides the location of the
+   *     Wasm binary and its loader.
+   * @param faceLandmarkerOptions The options for the FaceLandmarker.
+   *     Note that either a path to the model asset or a model buffer needs to
+   *     be provided (via `baseOptions`).
+   */
+  static createFromOptions(
+      wasmFileset: WasmFileset,
+      faceLandmarkerOptions: FaceLandmarkerOptions): Promise<FaceLandmarker> {
+    return VisionTaskRunner.createVisionInstance(
+        FaceLandmarker, wasmFileset, faceLandmarkerOptions);
+  }
+
+  /**
+   * Initializes the Wasm runtime and creates a new `FaceLandmarker` based on
+   * the provided model asset buffer.
+   * @param wasmFileset A configuration object that provides the location of the
+   *     Wasm binary and its loader.
+   * @param modelAssetBuffer A binary representation of the model.
+   */
+  static createFromModelBuffer(
+      wasmFileset: WasmFileset,
+      modelAssetBuffer: Uint8Array): Promise<FaceLandmarker> {
+    return VisionTaskRunner.createVisionInstance(
+        FaceLandmarker, wasmFileset, {baseOptions: {modelAssetBuffer}});
+  }
+
+  /**
+   * Initializes the Wasm runtime and creates a new `FaceLandmarker` based on
+   * the path to the model asset.
+   * @param wasmFileset A configuration object that provides the location of the
+   *     Wasm binary and its loader.
+   * @param modelAssetPath The path to the model asset.
+   */
+  static createFromModelPath(
+      wasmFileset: WasmFileset,
+      modelAssetPath: string): Promise<FaceLandmarker> {
+    return VisionTaskRunner.createVisionInstance(
+        FaceLandmarker, wasmFileset, {baseOptions: {modelAssetPath}});
+  }
+
+  /** @hideconstructor */
+  constructor(
+      wasmModule: WasmModule,
+      glCanvas?: HTMLCanvasElement|OffscreenCanvas|null) {
+    super(
+        new VisionGraphRunner(wasmModule, glCanvas), IMAGE_STREAM,
+        NORM_RECT_STREAM, /* roiAllowed= */ false);
+
+    this.options = new FaceLandmarkerGraphOptions();
+    this.options.setBaseOptions(new BaseOptionsProto());
+    this.faceLandmarksDetectorGraphOptions =
+        new FaceLandmarksDetectorGraphOptions();
+    this.options.setFaceLandmarksDetectorGraphOptions(
+        this.faceLandmarksDetectorGraphOptions);
+    this.faceDetectorGraphOptions = new FaceDetectorGraphOptions();
+    this.options.setFaceDetectorGraphOptions(this.faceDetectorGraphOptions);
+
+    this.initDefaults();
+  }
+
+  protected override get baseOptions(): BaseOptionsProto {
+    return this.options.getBaseOptions()!;
+  }
+
+  protected override set baseOptions(proto: BaseOptionsProto) {
+    this.options.setBaseOptions(proto);
+  }
+
+  /**
+   * Sets new options for this `FaceLandmarker`.
+   *
+   * Calling `setOptions()` with a subset of options only affects those options.
+   * You can reset an option back to its default value by explicitly setting it
+   * to `undefined`.
+   *
+   * @param options The options for the face landmarker.
+   */
+  override setOptions(options: FaceLandmarkerOptions): Promise<void> {
+    // Configure face detector options.
+    if ('numFaces' in options) {
+      this.faceDetectorGraphOptions.setNumFaces(
+          options.numFaces ?? DEFAULT_NUM_FACES);
+    }
+    if ('minFaceDetectionConfidence' in options) {
+      this.faceDetectorGraphOptions.setMinDetectionConfidence(
+          options.minFaceDetectionConfidence ?? DEFAULT_SCORE_THRESHOLD);
+    }
+
+    // Configure face landmark detector options.
+    if ('minTrackingConfidence' in options) {
+      this.options.setMinTrackingConfidence(
+          options.minTrackingConfidence ?? DEFAULT_SCORE_THRESHOLD);
+    }
+    if ('minFacePresenceConfidence' in options) {
+      this.faceLandmarksDetectorGraphOptions.setMinDetectionConfidence(
+          options.minFacePresenceConfidence ?? DEFAULT_SCORE_THRESHOLD);
+    }
+
+    if ('outputFaceBlendshapes' in options) {
+      this.outputFaceBlendshapes = !!options.outputFaceBlendshapes;
+    }
+
+    if ('outputFacialTransformationMatrixes' in options) {
+      this.outputFacialTransformationMatrixes =
+          !!options.outputFacialTransformationMatrixes;
+    }
+
+    return this.applyOptions(options);
+  }
+
+  /**
+   * Performs face landmarks detection on the provided single image and waits
+   * synchronously for the response. Only use this method when the
+   * FaceLandmarker is created with running mode `image`.
+   *
+   * @param image An image to process.
+   * @param imageProcessingOptions the `ImageProcessingOptions` specifying how
+   *    to process the input image before running inference.
+   * @return The detected face landmarks.
+   */
+  detect(image: ImageSource, imageProcessingOptions?: ImageProcessingOptions):
+      FaceLandmarkerResult {
+    this.resetResults();
+    this.processImageData(image, imageProcessingOptions);
+    return this.result;
+  }
+
+  /**
+   * Performs face landmarks detection on the provided video frame and waits
+   * synchronously for the response. Only use this method when the
+   * FaceLandmarker is created with running mode `video`.
+   *
+   * @param videoFrame A video frame to process.
+   * @param timestamp The timestamp of the current frame, in ms.
+   * @param imageProcessingOptions the `ImageProcessingOptions` specifying how
+   *    to process the input image before running inference.
+   * @return The detected face landmarks.
+   */
+  detectForVideo(
+      videoFrame: ImageSource, timestamp: number,
+      imageProcessingOptions?: ImageProcessingOptions): FaceLandmarkerResult {
+    this.resetResults();
+    this.processVideoData(videoFrame, imageProcessingOptions, timestamp);
+    return this.result;
+  }
+
+  private resetResults(): void {
+    this.result = {faceLandmarks: []};
+    if (this.outputFaceBlendshapes) {
+      this.result.faceBlendshapes = [];
+    }
+    if (this.outputFacialTransformationMatrixes) {
+      this.result.facialTransformationMatrixes = [];
+    }
+  }
+
+  /** Sets the default values for the graph. */
+  private initDefaults(): void {
+    this.faceDetectorGraphOptions.setNumFaces(DEFAULT_NUM_FACES);
+    this.faceDetectorGraphOptions.setMinDetectionConfidence(
+        DEFAULT_SCORE_THRESHOLD);
+    this.faceLandmarksDetectorGraphOptions.setMinDetectionConfidence(
+        DEFAULT_SCORE_THRESHOLD);
+    this.options.setMinTrackingConfidence(DEFAULT_SCORE_THRESHOLD);
+  }
+
+  /** Adds new face landmark from the given proto. */
+  private addJsLandmarks(data: Uint8Array[]): void {
+    for (const binaryProto of data) {
+      const faceLandmarksProto =
+          NormalizedLandmarkListProto.deserializeBinary(binaryProto);
+      const landmarks: NormalizedLandmark[] = [];
+      for (const faceLandmarkProto of faceLandmarksProto.getLandmarkList()) {
+        landmarks.push({
+          x: faceLandmarkProto.getX() ?? 0,
+          y: faceLandmarkProto.getY() ?? 0,
+          z: faceLandmarkProto.getZ() ?? 0,
+        });
+      }
+      this.result.faceLandmarks.push(landmarks);
+    }
+  }
+
+  /** Adds new blendshapes from the given proto. */
+  private addBlenshape(data: Uint8Array[]): void {
+    if (!this.result.faceBlendshapes) {
+      return;
+    }
+
+    for (const binaryProto of data) {
+      const classificationList =
+          ClassificationListProto.deserializeBinary(binaryProto);
+      this.result.faceBlendshapes.push(convertFromClassifications(
+          classificationList.getClassificationList() ?? []));
+    }
+  }
+
+  /** Adds new transformation matrixes from the given proto. */
+  private addFacialTransformationMatrixes(data: Uint8Array[]): void {
+    if (!this.result.facialTransformationMatrixes) {
+      return;
+    }
+
+    for (const binaryProto of data) {
+      const faceGeometryProto =
+          FaceGeometryProto.deserializeBinary(binaryProto);
+      const poseTransformMatrix = faceGeometryProto.getPoseTransformMatrix();
+      if (poseTransformMatrix) {
+        this.result.facialTransformationMatrixes.push({
+          rows: poseTransformMatrix.getRows() ?? 0,
+          columns: poseTransformMatrix.getCols() ?? 0,
+          data: poseTransformMatrix.getPackedDataList() ?? [],
+        });
+      }
+    }
+  }
+
+  /** Updates the MediaPipe graph configuration. */
+  protected override refreshGraph(): void {
+    const graphConfig = new CalculatorGraphConfig();
+    graphConfig.addInputStream(IMAGE_STREAM);
+    graphConfig.addInputStream(NORM_RECT_STREAM);
+    graphConfig.addOutputStream(LANDMARKS_STREAM);
+
+    const calculatorOptions = new CalculatorOptions();
+    calculatorOptions.setExtension(
+        FaceLandmarkerGraphOptions.ext, this.options);
+
+    const landmarkerNode = new CalculatorGraphConfig.Node();
+    landmarkerNode.setCalculator(FACE_LANDMARKER_GRAPH);
+    landmarkerNode.addInputStream('IMAGE:' + IMAGE_STREAM);
+    landmarkerNode.addInputStream('NORM_RECT:' + NORM_RECT_STREAM);
+    landmarkerNode.addOutputStream('NORM_LANDMARKS:' + LANDMARKS_STREAM);
+    landmarkerNode.setOptions(calculatorOptions);
+
+    graphConfig.addNode(landmarkerNode);
+
+    this.graphRunner.attachProtoVectorListener(
+        LANDMARKS_STREAM, (binaryProto, timestamp) => {
+          this.addJsLandmarks(binaryProto);
+          this.setLatestOutputTimestamp(timestamp);
+        });
+    this.graphRunner.attachEmptyPacketListener(
+      LANDMARKS_STREAM, timestamp => {
+          this.setLatestOutputTimestamp(timestamp);
+        });
+
+    if (this.outputFaceBlendshapes) {
+      graphConfig.addOutputStream(BLENDSHAPES_STREAM);
+      landmarkerNode.addOutputStream('BLENDSHAPES:' + BLENDSHAPES_STREAM);
+      this.graphRunner.attachProtoVectorListener(
+          BLENDSHAPES_STREAM, (binaryProto, timestamp) => {
+            this.addBlenshape(binaryProto);
+            this.setLatestOutputTimestamp(timestamp);
+          });
+      this.graphRunner.attachEmptyPacketListener(
+          BLENDSHAPES_STREAM, timestamp => {
+            this.setLatestOutputTimestamp(timestamp);
+          });
+    }
+
+    if (this.outputFacialTransformationMatrixes) {
+      graphConfig.addOutputStream(FACE_GEOMETRY_STREAM);
+      landmarkerNode.addOutputStream('FACE_GEOMETRY:' + FACE_GEOMETRY_STREAM);
+
+      this.graphRunner.attachProtoVectorListener(
+          FACE_GEOMETRY_STREAM, (binaryProto, timestamp) => {
+            this.addFacialTransformationMatrixes(binaryProto);
+            this.setLatestOutputTimestamp(timestamp);
+          });
+      this.graphRunner.attachEmptyPacketListener(
+          FACE_GEOMETRY_STREAM, timestamp => {
+            this.setLatestOutputTimestamp(timestamp);
+          });
+    }
+
+    const binaryGraph = graphConfig.serializeBinary();
+    this.setGraph(new Uint8Array(binaryGraph), /* isBinary= */ true);
+  }
+}
+
+
diff --git a/mediapipe/tasks/web/vision/face_landmarker/face_landmarker_options.d.ts b/mediapipe/tasks/web/vision/face_landmarker/face_landmarker_options.d.ts
new file mode 100644
index 000000000..f537ca2f6
--- /dev/null
+++ b/mediapipe/tasks/web/vision/face_landmarker/face_landmarker_options.d.ts
@@ -0,0 +1,58 @@
+/**
+ * Copyright 2023 The MediaPipe Authors. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+import {VisionTaskOptions} from '../../../../tasks/web/vision/core/vision_task_options';
+
+/** Options to configure the MediaPipe FaceLandmarker Task */
+export declare interface FaceLandmarkerOptions extends VisionTaskOptions {
+  /**
+   * The maximum number of faces can be detected by the FaceLandmarker.
+   * Defaults to 1.
+   */
+  numFaces?: number|undefined;
+
+  /**
+   * The minimum confidence score for the face detection to be considered
+   * successful. Defaults to 0.5.
+   */
+  minFaceDetectionConfidence?: number|undefined;
+
+  /**
+   * The minimum confidence score of face presence score in the face landmark
+   * detection. Defaults to 0.5.
+   */
+  minFacePresenceConfidence?: number|undefined;
+
+  /**
+   * The minimum confidence score for the face tracking to be considered
+   * successful. Defaults to 0.5.
+   */
+  minTrackingConfidence?: number|undefined;
+
+  /**
+   * Whether FaceLandmarker outputs face blendshapes classification. Face
+   * blendshapes are used for rendering the 3D face model.
+   */
+  outputFaceBlendshapes?: boolean|undefined;
+
+  /**
+   * Whether FaceLandmarker outputs facial transformation_matrix. Facial
+   * transformation matrix is used to transform the face landmarks in canonical
+   * face to the detected face, so that users can apply face effects on the
+   * detected landmarks.
+   */
+  outputFacialTransformationMatrixes?: boolean|undefined;
+}
diff --git a/mediapipe/tasks/web/vision/face_landmarker/face_landmarker_result.d.ts b/mediapipe/tasks/web/vision/face_landmarker/face_landmarker_result.d.ts
new file mode 100644
index 000000000..123c0a82a
--- /dev/null
+++ b/mediapipe/tasks/web/vision/face_landmarker/face_landmarker_result.d.ts
@@ -0,0 +1,36 @@
+/**
+ * Copyright 2023 The MediaPipe Authors. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+import {Category} from '../../../../tasks/web/components/containers/category';
+import {Classifications} from '../../../../tasks/web/components/containers/classification_result';
+import {Landmark, NormalizedLandmark} from '../../../../tasks/web/components/containers/landmark';
+import {Matrix} from '../../../../tasks/web/components/containers/matrix';
+
+export {Category, Landmark, NormalizedLandmark};
+
+/**
+ * Represents the face landmarks deection results generated by `FaceLandmarker`.
+ */
+export declare interface FaceLandmarkerResult {
+  /** Detected face landmarks in normalized image coordinates. */
+  faceLandmarks: NormalizedLandmark[][];
+
+  /** Optional face blendshapes results. */
+  faceBlendshapes?: Classifications[];
+
+  /** Optional facial transformation matrix. */
+  facialTransformationMatrixes?: Matrix[];
+}
diff --git a/mediapipe/tasks/web/vision/face_landmarker/face_landmarker_test.ts b/mediapipe/tasks/web/vision/face_landmarker/face_landmarker_test.ts
new file mode 100644
index 000000000..92012a6f3
--- /dev/null
+++ b/mediapipe/tasks/web/vision/face_landmarker/face_landmarker_test.ts
@@ -0,0 +1,306 @@
+/**
+ * Copyright 2023 The MediaPipe Authors. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+import 'jasmine';
+
+import {CalculatorGraphConfig} from '../../../../framework/calculator_pb';
+import {Classification, ClassificationList} from '../../../../framework/formats/classification_pb';
+import {NormalizedLandmark, NormalizedLandmarkList} from '../../../../framework/formats/landmark_pb';
+import {MatrixData as MatrixDataProto} from '../../../../framework/formats/matrix_data_pb';
+import {FaceGeometry as FaceGeometryProto} from '../../../../tasks/cc/vision/face_geometry/proto/face_geometry_pb';
+import {addJasmineCustomFloatEqualityTester, createSpyWasmModule, MediapipeTasksFake, SpyWasmModule, verifyGraph, verifyListenersRegistered} from '../../../../tasks/web/core/task_runner_test_utils';
+import {VisionGraphRunner} from '../../../../tasks/web/vision/core/vision_task_runner';
+
+import {FaceLandmarker} from './face_landmarker';
+import {FaceLandmarkerOptions} from './face_landmarker_options';
+
+// The OSS JS API does not support the builder pattern.
+// tslint:disable:jspb-use-builder-pattern
+
+type ProtoListener = ((binaryProtos: Uint8Array[], timestamp: number) => void);
+
+function createBlendshapes(): Uint8Array[] {
+  const blendshapesProto = new ClassificationList();
+  const classification = new Classification();
+  classification.setScore(0.1);
+  classification.setIndex(1);
+  classification.setLabel('face_label');
+  classification.setDisplayName('face_display_name');
+  blendshapesProto.addClassification(classification);
+  return [blendshapesProto.serializeBinary()];
+}
+
+function createFacialTransformationMatrixes(): Uint8Array[] {
+  const faceGeometryProto = new FaceGeometryProto();
+  const posteTransformationMatrix = new MatrixDataProto();
+  posteTransformationMatrix.setRows(1);
+  posteTransformationMatrix.setCols(1);
+  posteTransformationMatrix.setPackedDataList([1.0]);
+  faceGeometryProto.setPoseTransformMatrix(posteTransformationMatrix);
+  return [faceGeometryProto.serializeBinary()];
+}
+
+function createLandmarks(): Uint8Array[] {
+  const faceLandmarksProto = new NormalizedLandmarkList();
+  const landmark = new NormalizedLandmark();
+  landmark.setX(0.3);
+  landmark.setY(0.4);
+  landmark.setZ(0.5);
+  faceLandmarksProto.addLandmark(landmark);
+  return [faceLandmarksProto.serializeBinary()];
+}
+
+class FaceLandmarkerFake extends FaceLandmarker implements MediapipeTasksFake {
+  calculatorName = 'mediapipe.tasks.vision.face_landmarker.FaceLandmarkerGraph';
+  attachListenerSpies: jasmine.Spy[] = [];
+  graph: CalculatorGraphConfig|undefined;
+  fakeWasmModule: SpyWasmModule;
+  listeners = new Map<string, ProtoListener>();
+
+  constructor() {
+    super(createSpyWasmModule(), /* glCanvas= */ null);
+    this.fakeWasmModule =
+        this.graphRunner.wasmModule as unknown as SpyWasmModule;
+
+    this.attachListenerSpies[0] =
+        spyOn(this.graphRunner, 'attachProtoVectorListener')
+            .and.callFake((stream, listener) => {
+              expect(stream).toMatch(
+                  /(face_landmarks|blendshapes|face_geometry)/);
+              this.listeners.set(stream, listener);
+            });
+
+    spyOn(this.graphRunner, 'setGraph').and.callFake(binaryGraph => {
+      this.graph = CalculatorGraphConfig.deserializeBinary(binaryGraph);
+    });
+    spyOn(this.graphRunner, 'addGpuBufferAsImageToStream');
+    spyOn(this.graphRunner, 'addProtoToStream');
+  }
+
+  getGraphRunner(): VisionGraphRunner {
+    return this.graphRunner;
+  }
+}
+
+describe('FaceLandmarker', () => {
+  let faceLandmarker: FaceLandmarkerFake;
+
+  beforeEach(async () => {
+    addJasmineCustomFloatEqualityTester();
+    faceLandmarker = new FaceLandmarkerFake();
+    await faceLandmarker.setOptions(
+        {baseOptions: {modelAssetBuffer: new Uint8Array([])}});
+  });
+
+  it('initializes graph', async () => {
+    verifyGraph(faceLandmarker);
+    verifyListenersRegistered(faceLandmarker);
+  });
+
+  it('reloads graph when settings are changed', async () => {
+    verifyListenersRegistered(faceLandmarker);
+
+    await faceLandmarker.setOptions({numFaces: 1});
+    verifyGraph(faceLandmarker, [['faceDetectorGraphOptions', 'numFaces'], 1]);
+    verifyListenersRegistered(faceLandmarker);
+
+    await faceLandmarker.setOptions({numFaces: 5});
+    verifyGraph(faceLandmarker, [['faceDetectorGraphOptions', 'numFaces'], 5]);
+    verifyListenersRegistered(faceLandmarker);
+  });
+
+  it('merges options', async () => {
+    await faceLandmarker.setOptions({numFaces: 1});
+    await faceLandmarker.setOptions({minFaceDetectionConfidence: 0.5});
+    verifyGraph(faceLandmarker, [
+      'faceDetectorGraphOptions', {
+        numFaces: 1,
+        baseOptions: undefined,
+        minDetectionConfidence: 0.5,
+        minSuppressionThreshold: 0.5
+      }
+    ]);
+  });
+
+  describe('setOptions()', () => {
+    interface TestCase {
+      optionPath: [keyof FaceLandmarkerOptions, ...string[]];
+      fieldPath: string[];
+      customValue: unknown;
+      defaultValue: unknown;
+    }
+
+    const testCases: TestCase[] = [
+      {
+        optionPath: ['numFaces'],
+        fieldPath: ['faceDetectorGraphOptions', 'numFaces'],
+        customValue: 5,
+        defaultValue: 1
+      },
+      {
+        optionPath: ['minFaceDetectionConfidence'],
+        fieldPath: ['faceDetectorGraphOptions', 'minDetectionConfidence'],
+        customValue: 0.1,
+        defaultValue: 0.5
+      },
+      {
+        optionPath: ['minFacePresenceConfidence'],
+        fieldPath:
+            ['faceLandmarksDetectorGraphOptions', 'minDetectionConfidence'],
+        customValue: 0.2,
+        defaultValue: 0.5
+      },
+      {
+        optionPath: ['minTrackingConfidence'],
+        fieldPath: ['minTrackingConfidence'],
+        customValue: 0.3,
+        defaultValue: 0.5
+      },
+    ];
+
+    /** Creates an options object that can be passed to setOptions() */
+    function createOptions(
+        path: string[], value: unknown): FaceLandmarkerOptions {
+      const options: Record<string, unknown> = {};
+      let currentLevel = options;
+      for (const element of path.slice(0, -1)) {
+        currentLevel[element] = {};
+        currentLevel = currentLevel[element] as Record<string, unknown>;
+      }
+      currentLevel[path[path.length - 1]] = value;
+      return options;
+    }
+
+    for (const testCase of testCases) {
+      it(`uses default value for ${testCase.optionPath[0]}`, async () => {
+        verifyGraph(
+            faceLandmarker, [testCase.fieldPath, testCase.defaultValue]);
+      });
+
+      it(`can set ${testCase.optionPath[0]}`, async () => {
+        await faceLandmarker.setOptions(
+            createOptions(testCase.optionPath, testCase.customValue));
+        verifyGraph(faceLandmarker, [testCase.fieldPath, testCase.customValue]);
+      });
+
+      it(`can clear ${testCase.optionPath[0]}`, async () => {
+        await faceLandmarker.setOptions(
+            createOptions(testCase.optionPath, testCase.customValue));
+        verifyGraph(faceLandmarker, [testCase.fieldPath, testCase.customValue]);
+
+        await faceLandmarker.setOptions(
+            createOptions(testCase.optionPath, undefined));
+        verifyGraph(
+            faceLandmarker, [testCase.fieldPath, testCase.defaultValue]);
+      });
+    }
+
+    it('supports outputFaceBlendshapes', async () => {
+      const stream = 'blendshapes';
+      await faceLandmarker.setOptions({});
+      expect(faceLandmarker.graph!.getOutputStreamList()).not.toContain(stream);
+
+      await faceLandmarker.setOptions({outputFaceBlendshapes: false});
+      expect(faceLandmarker.graph!.getOutputStreamList()).not.toContain(stream);
+
+      await faceLandmarker.setOptions({outputFaceBlendshapes: true});
+      expect(faceLandmarker.graph!.getOutputStreamList()).toContain(stream);
+    });
+
+    it('supports outputFacialTransformationMatrixes', async () => {
+      const stream = 'face_geometry';
+      await faceLandmarker.setOptions({});
+      expect(faceLandmarker.graph!.getOutputStreamList()).not.toContain(stream);
+
+      await faceLandmarker.setOptions(
+          {outputFacialTransformationMatrixes: false});
+      expect(faceLandmarker.graph!.getOutputStreamList()).not.toContain(stream);
+
+      await faceLandmarker.setOptions(
+          {outputFacialTransformationMatrixes: true});
+      expect(faceLandmarker.graph!.getOutputStreamList()).toContain(stream);
+    });
+  });
+
+  it('doesn\'t support region of interest', () => {
+    expect(() => {
+      faceLandmarker.detect(
+          {} as HTMLImageElement,
+          {regionOfInterest: {left: 0, right: 0, top: 0, bottom: 0}});
+    }).toThrowError('This task doesn\'t support region-of-interest.');
+  });
+
+  it('transforms results', async () => {
+    // Pass the test data to our listener
+    faceLandmarker.fakeWasmModule._waitUntilIdle.and.callFake(() => {
+      verifyListenersRegistered(faceLandmarker);
+      faceLandmarker.listeners.get('face_landmarks')!(createLandmarks(), 1337);
+      faceLandmarker.listeners.get('blendshapes')!(createBlendshapes(), 1337);
+      faceLandmarker.listeners.get('face_geometry')!
+          (createFacialTransformationMatrixes(), 1337);
+    });
+
+    await faceLandmarker.setOptions({
+      outputFaceBlendshapes: true,
+      outputFacialTransformationMatrixes: true
+    });
+
+    // Invoke the face landmarker
+    const landmarks = faceLandmarker.detect({} as HTMLImageElement);
+    expect(faceLandmarker.getGraphRunner().addProtoToStream)
+        .toHaveBeenCalledTimes(1);
+    expect(faceLandmarker.getGraphRunner().addGpuBufferAsImageToStream)
+        .toHaveBeenCalledTimes(1);
+    expect(faceLandmarker.fakeWasmModule._waitUntilIdle).toHaveBeenCalled();
+
+    expect(landmarks).toEqual({
+      faceLandmarks: [[{x: 0.3, y: 0.4, z: 0.5}]],
+      faceBlendshapes: [{
+        categories: [{
+          index: 1,
+          score: 0.1,
+          categoryName: 'face_label',
+          displayName: 'face_display_name'
+        }],
+        headIndex: -1,
+        headName: ''
+      }],
+      facialTransformationMatrixes: [({rows: 1, columns: 1, data: [1]})]
+    });
+  });
+
+  it('clears results between invoations', async () => {
+    // Pass the test data to our listener
+    faceLandmarker.fakeWasmModule._waitUntilIdle.and.callFake(() => {
+      faceLandmarker.listeners.get('face_landmarks')!(createLandmarks(), 1337);
+      faceLandmarker.listeners.get('blendshapes')!(createBlendshapes(), 1337);
+      faceLandmarker.listeners.get('face_geometry')!
+          (createFacialTransformationMatrixes(), 1337);
+    });
+
+    await faceLandmarker.setOptions({
+      outputFaceBlendshapes: true,
+      outputFacialTransformationMatrixes: true
+    });
+
+    // Invoke the face landmarker twice
+    const landmarks1 = faceLandmarker.detect({} as HTMLImageElement);
+    const landmarks2 = faceLandmarker.detect({} as HTMLImageElement);
+
+    // Verify that faces2 is not a concatenation of all previously returned
+    // faces.
+    expect(landmarks1).toEqual(landmarks2);
+  });
+});
diff --git a/mediapipe/tasks/web/vision/index.ts b/mediapipe/tasks/web/vision/index.ts
index 7fca725ec..856d84683 100644
--- a/mediapipe/tasks/web/vision/index.ts
+++ b/mediapipe/tasks/web/vision/index.ts
@@ -15,6 +15,7 @@
  */
 
 import {FilesetResolver as FilesetResolverImpl} from '../../../tasks/web/core/fileset_resolver';
+import {FaceLandmarker as FaceLandmarkerImpl} from '../../../tasks/web/vision/face_landmarker/face_landmarker';
 import {FaceStylizer as FaceStylizerImpl} from '../../../tasks/web/vision/face_stylizer/face_stylizer';
 import {GestureRecognizer as GestureRecognizerImpl} from '../../../tasks/web/vision/gesture_recognizer/gesture_recognizer';
 import {HandLandmarker as HandLandmarkerImpl} from '../../../tasks/web/vision/hand_landmarker/hand_landmarker';
@@ -27,6 +28,7 @@ import {ObjectDetector as ObjectDetectorImpl} from '../../../tasks/web/vision/ob
 // Declare the variables locally so that Rollup in OSS includes them explicitly
 // as exports.
 const FilesetResolver = FilesetResolverImpl;
+const FaceLandmarker = FaceLandmarkerImpl;
 const FaceStylizer = FaceStylizerImpl;
 const GestureRecognizer = GestureRecognizerImpl;
 const HandLandmarker = HandLandmarkerImpl;
@@ -38,6 +40,7 @@ const ObjectDetector = ObjectDetectorImpl;
 
 export {
   FilesetResolver,
+  FaceLandmarker,
   FaceStylizer,
   GestureRecognizer,
   HandLandmarker,