* **[Full Face Detection and Recognition Pipeline](#usage-full-face-detection-and-recognition-pipeline)**

The Tiny Yolo v2 based face detector can easily adapt to different input image sizes, thus can be used as an alternative to SSD Mobilenet v1 to trade off accuracy for performance (inference time). In general the model is not as accurate as SSD Mobilenet v1 but can achieve faster inference for lower image sizes.

The Tiny Yolo v2 implementation is still experimental, meaning there is room for optimization (future work). The trained model weights are provided in the [azFace](https://github.com/azmathmoosa/azFace) project.

MTCNN (Multi-task Cascaded Convolutional Neural Networks) represents an alternative face detector to SSD Mobilenet v1 and Tiny Yolo v2, which offers much more room for configuration and is able to achieve much lower processing times. MTCNN is a 3 stage cascaded CNN, which simultanously returns 5 face landmark points along with the bounding boxes and scores for each face. By limiting the minimum size of faces expected in an image, MTCNN allows you to process frames from your webcam in realtime. Additionally with 2MB, the size of the weights file is only a third of the size of the quantized SSD Mobilenet v1 model (~6MB).

const detections = await faceapi.locateFaces(myImg, minConfidence, maxResults)

const detections = await faceapi.locateFaces(input)

<a name="usage-full-face-detection-and-recognition-pipeline"></a>

After face detection has been performed, I would recommend to align the bounding boxes of the detected faces before passing them to the face recognition net, which will make the computed face descriptor much more accurate. Fortunately, the api can do this for you under the hood. You can obtain the full face descriptions (location, landmarks and descriptor) of each face in an input image as follows:

``` javascript

const fullFaceDescriptions = await faceapi.allFaces(input, minConfidence)

const fullFaceDescription0 = fullFaceDescriptions[0]

console.log(fullFaceDescription0.detection) // bounding box & score

console.log(fullFaceDescription0.landmarks) // 68 point face landmarks

console.log(fullFaceDescription0.descriptor) // face descriptor

You can also do everything manually as shown in the following:

const detections = await faceapi.locateFaces(input, minConfidence)

const faceTensors = (await faceapi.extractFaceTensors(input, detections))

const alignedFaceBoxes = await Promise.all(faceTensors.map(

async (faceTensor, i) => {

const faceLandmarks = await faceapi.detectLandmarks(faceTensor)

return faceLandmarks.align(detections[i])

}

))

faceTensors.forEach(t => t.dispose())

const alignedFaceTensors = (await faceapi.extractFaceTensors(input, alignedFaceBoxes))

const descriptors = await Promise.all(alignedFaceTensors.map(

faceTensor => faceapi.computeFaceDescriptor(faceTensor)

))

alignedFaceTensors.forEach(t => t.dispose())

function displayTimeStats(timeInMs) {

$('#time').val(`${timeInMs} ms`)

$('#fps').val(`${faceapi.round(1000 / timeInMs)}`)

}

displayTimeStats(Date.now() - ts)

function displayTimeStats(timeInMs) {

$('#time').val(`${timeInMs} ms`)

$('#fps').val(`${faceapi.round(1000 / timeInMs)}`)

}

displayTimeStats(Date.now() - ts)

function displayTimeStats(timeInMs) {

$('#time').val(`${timeInMs} ms`)

$('#fps').val(`${faceapi.round(1000 / timeInMs)}`)

}

displayTimeStats(Date.now() - ts)

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