opentok-faceid

1. sendScreenShot (front-end js) method uses subsciber.getImgData() method to capture screenshot of the subscriber (remote participant) and post it to the server.
2. On the server we use detectFace() method to detect the facial features in this image and we get a identifier for the detected face (id1)
3. we use detectFace() again with the image we want to compare with and we get identifier for the detected face (id2)
4. we use verifyFace() method and pass id1 and id2 as the inputs. Microsoft face API compares these two faces and provides a result that includes match/mismatch as well as a score.

Reference - 
1. getImgData() - https://tokbox.com/developer/sdks/js/reference/Subscriber.html#getImgData
2. Microsoft Face API - https://westus.dev.cognitive.microsoft.com/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395236


function sendScreenShot() {
    var imgdata = undefined;
    if (subscriber) {
        imgdata = subscriber.getImgData();
    }
    if (imgdata != undefined) {
        try {
            var blob = this.b64toBlob(imgdata, "image/png");
            let formData = new FormData();
            formData.append('customer', blob);
            let res = await $HTTPDEMO.post('/faceIDDemo.php',
                formData, {
                    headers: {
                        'Content-Type': 'multipart/form-data'
                    }

                }
            );
            console.log(res.data);
            if (res.data.status != "success") {
                alert("Error uploading the file");
            } else {
            
            }
        } catch (error) {
            alert("error posting screenshot");
            console.log(error);
        }
    }
}

function b64toBlob(b64Data, contentType, sliceSize) {
    contentType = contentType || '';
    sliceSize = sliceSize || 512;
    var byteCharacters = atob(b64Data);
    var byteArrays = [];
    for (var offset = 0; offset < byteCharacters.length; offset += sliceSize) {
        var slice = byteCharacters.slice(offset, offset + sliceSize);
        var byteNumbers = new Array(slice.length);
        for (var i = 0; i < slice.length; i++) {
            byteNumbers[i] = slice.charCodeAt(i);
        }
        var byteArray = new Uint8Array(byteNumbers);
        byteArrays.push(byteArray);
    }
    var blob = new Blob(byteArrays, {
        type: contentType
    });
    return blob;
}


$faceid_endpoint = "https://southeastasia.api.cognitive.microsoft.com";
$faceid_key = "your-key";

function detectFace($img){
        global $faceid_endpoint, $data_dir_url,$faceid_key;
        
        $client = new GuzzleHttp\Client([
            'base_uri' => $faceid_endpoint
        ]);

        $resp = $client->request('POST', 'face/v1.0/detect?recognitionModel=recognition_02&detectionModel=detection_02', [
            'headers' => [
                'Content-Type' => 'application/json',
                'Ocp-Apim-Subscription-Key' => $faceid_key
            ],
            'json' => ['url'=> $data_dir_url.$img]
        ]);

        $json = json_decode($resp->getBody(),true);
        //echo $resp->getBody();
        return $json[0];
}


        
function verifyFace($id1,$id2){
        global $faceid_endpoint, $data_dir_url,$faceid_key;
        $client = new GuzzleHttp\Client([
            'base_uri' => $faceid_endpoint
        ]);

        $resp = $client->request('POST', 'face/v1.0/verify', [
            'headers' => [
                'Content-Type' => 'application/json',
                'Ocp-Apim-Subscription-Key' => $faceid_key
            ],
            'json' => [
                'faceid1'=>$id1,
                'faceid2'=>$id2
            ]
        ]);

        return $resp->getBody();
}