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‎README.md

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@@ -16,7 +16,7 @@ Check out my article **[face-api.js — JavaScript API for Face Recognition
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* **[Loading the Models](#usage-load-models)**
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* **[Face Detection - SSD Mobilenet v1](#usage-face-detection-ssd)**
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* **[Face Detection & 5 Point Face Landmarks - MTCNN](#usage-face-detection-mtcnn)**
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* **[Face Recognition - FaceNet](#usage-face-recognition)**
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* **[Face Recognition](#usage-face-recognition)**
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* **[68 Point Face Landmark Detection](#usage-face-landmark-detection)**
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* **[Full Face Detection and Recognition Pipeline](#usage-full-face-detection-and-recognition-pipeline)**
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<a name="about-face-recognition"></a>
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### Face Recognition - FaceNet
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### Face Recognition
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For face recognition, a ResNet-34 like architecture is implemented to compute a face descriptor (a feature vector with 128 values) from any given face image, which is used to describe the characteristics of a persons face. The model is **not** limited to the set of faces used for training, meaning you can use it for face recognition of any person, for example yourself. You can determine the similarity of two arbitrary faces by comparing their face descriptors, for example by computing the euclidean distance or using any other classifier of your choice.
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‎build/FaceDetection.d.ts

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import { Rect } from './Rect';
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import { Dimensions } from './types';
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export declare class FaceDetection {
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private _score;
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private _box;
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private _imageWidth;
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private _imageHeight;
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constructor(score: number, relativeBox: Rect, imageDims: Dimensions);
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readonly score: number;
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readonly box: Rect;
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readonly imageWidth: number;
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readonly imageHeight: number;
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readonly relativeBox: Rect;
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getScore(): number;
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getBox(): Rect;
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getImageWidth(): number;
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getImageHeight(): number;
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getRelativeBox(): Rect;
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forSize(width: number, height: number): FaceDetection;
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}

‎build/FaceDetection.js

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‎build/FaceDetection.js.map

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‎build/FaceLandmarks.d.ts

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import { Point } from './Point';
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import { Dimensions } from './types';
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export declare class FaceLandmarks {
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protected _imageWidth: number;
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protected _imageHeight: number;
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protected _shift: Point;
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protected _faceLandmarks: Point[];
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constructor(relativeFaceLandmarkPositions: Point[], imageDims: Dimensions, shift?: Point);
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getShift(): Point;
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getImageWidth(): number;
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getImageHeight(): number;
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getPositions(): Point[];
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getRelativePositions(): Point[];
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}

‎build/FaceLandmarks.js

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‎build/FaceLandmarks.js.map

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‎build/FullFaceDescription.d.ts

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import { FaceDetection } from './faceDetectionNet/FaceDetection';
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import { FaceLandmarks } from './faceLandmarkNet/FaceLandmarks';
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import { FaceDetection } from './FaceDetection';
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import { FaceLandmarks68 } from './faceLandmarkNet/FaceLandmarks68';
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export declare class FullFaceDescription {
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private _detection;
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private _landmarks;
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private _descriptor;
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constructor(_detection: FaceDetection, _landmarks: FaceLandmarks, _descriptor: Float32Array);
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constructor(_detection: FaceDetection, _landmarks: FaceLandmarks68, _descriptor: Float32Array);
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readonly detection: FaceDetection;
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readonly landmarks: FaceLandmarks;
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readonly landmarks: FaceLandmarks68;
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readonly descriptor: Float32Array;
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forSize(width: number, height: number): FullFaceDescription;
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}

‎build/FullFaceDescription.js.map

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‎build/NetInput.d.ts

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import { TResolvedNetInput } from './types';
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export declare class NetInput {
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private _inputs;
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private _canvases;
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private _isManaged;
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private _isBatchInput;
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private _inputDimensions;
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private _paddings;
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constructor(inputs: tf.Tensor4D | Array<TResolvedNetInput>, isBatchInput?: boolean);
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constructor(inputs: tf.Tensor4D | Array<TResolvedNetInput>, isBatchInput?: boolean, keepCanvases?: boolean);
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readonly inputs: tf.Tensor3D[];
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readonly canvases: HTMLCanvasElement[];
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readonly isManaged: boolean;
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readonly isBatchInput: boolean;
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readonly batchSize: number;

‎build/NetInput.js

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