docs: add quixzoom-auth-core product to AAMOS
- Product documentation in docs/products/ - Updated MEMORY.md with product info - quiXzoom Auth Core as AAMOS Identity product
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/**
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* POST /v1/authenticate — Autentisering/bedrägeri
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* Kombinerar face verification + liveness + risk scoring
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*/
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import { Router } from 'express';
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import * as ort from 'onnxruntime-node';
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import sharp from 'sharp';
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import { fetchImage, hashInput, saveResult, genReqId, requireAuth } from './utils.mjs';
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const router = Router();
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const FACE_DET_MODEL = '/opt/amos/data/kyc-service/models/face_detection_yunet_2023mar.onnx';
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const LIVENESS_MODEL = '/opt/amos/data/kyc-service/models/2.7_80x80_MiniFASNetV2.onnx';
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const FACE_REC_MODEL = '/opt/amos/data/kyc-service/models/face_recognition_sface_2021dec.onnx';
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let detSession = null, liveSession = null, recSession = null;
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async function getDetSession() {
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if (!detSession) detSession = await ort.InferenceSession.create(FACE_DET_MODEL);
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return detSession;
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}
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async function getLiveSession() {
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if (!liveSession) liveSession = await ort.InferenceSession.create(LIVENESS_MODEL);
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return liveSession;
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}
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async function getRecSession() {
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if (!recSession) recSession = await ort.InferenceSession.create(FACE_REC_MODEL);
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return recSession;
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}
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async function detectFace(buffer) {
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const raw = await sharp(buffer).resize(640, 640).raw().toBuffer({ resolveWithObject: true });
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const { data, info } = raw;
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const h = info.height, w = info.width;
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const floatData = new Float32Array(1 * 3 * h * w);
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for (let y = 0; y < h; y++) {
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for (let x = 0; x < w; x++) {
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const idx = (y * w + x) * 3;
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floatData[0 * h * w + y * w + x] = data[idx] / 255.0;
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floatData[1 * h * w + y * w + x] = data[idx + 1] / 255.0;
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floatData[2 * h * w + y * w + x] = data[idx + 2] / 255.0;
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}
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}
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const tensor = new ort.Tensor('float32', floatData, [1, 3, h, w]);
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const sess = await getDetSession();
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const feeds = {}; feeds[sess.inputNames[0]] = tensor;
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const out = await sess.run(feeds);
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const outTensor = out[sess.outputNames[0]];
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const outData = outTensor.data;
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const dims = outTensor.dims;
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const stride = dims[dims.length - 1];
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let bestScore = 0, bestBox = null;
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for (let i = 0; i < dims[0]; i++) {
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const row = Array.from(outData.slice(i * stride, (i + 1) * stride));
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if (row[2] > bestScore) { bestScore = row[2]; bestBox = row; }
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}
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if (!bestBox || bestScore < 0.5) return null;
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return { box: bestBox, score: bestScore };
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}
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async function checkLiveness(buffer, box) {
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const orig = await sharp(buffer).raw().toBuffer({ resolveWithObject: true });
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const ow = orig.info.width, oh = orig.info.height;
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const x1 = Math.max(0, Math.round(box[3] * ow));
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const y1 = Math.max(0, Math.round(box[4] * oh));
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const x2 = Math.min(ow, Math.round(box[5] * ow));
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const y2 = Math.min(oh, Math.round(box[6] * oh));
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const faceBuf = await sharp(buffer)
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.extract({ left: x1, top: y1, width: x2 - x1, height: y2 - y1 })
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.resize(80, 80)
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.raw()
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.toBuffer();
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const liveFloat = new Float32Array(1 * 3 * 80 * 80);
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for (let i = 0; i < 80 * 80; i++) {
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liveFloat[0 * 6400 + i] = faceBuf[i * 3] / 255.0;
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liveFloat[1 * 6400 + i] = faceBuf[i * 3 + 1] / 255.0;
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liveFloat[2 * 6400 + i] = faceBuf[i * 3 + 2] / 255.0;
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}
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const liveTensor = new ort.Tensor('float32', liveFloat, [1, 3, 80, 80]);
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const liveSess = await getLiveSession();
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const liveFeeds = {}; liveFeeds[liveSess.inputNames[0]] = liveTensor;
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const liveOut = await liveSess.run(liveFeeds);
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const liveData = liveOut[liveSess.outputNames[0]].data;
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const realScore = liveData[0];
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const fakeScore = liveData[1];
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const score = realScore / (realScore + fakeScore + 1e-6);
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return { score: parseFloat(score.toFixed(4)), label: score > 0.7 ? 'live' : score > 0.4 ? 'uncertain' : 'spoof' };
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}
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async function getEmbedding(buffer, box) {
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const orig = await sharp(buffer).raw().toBuffer({ resolveWithObject: true });
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const ow = orig.info.width, oh = orig.info.height;
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const x1 = Math.max(0, Math.round(box[3] * ow));
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const y1 = Math.max(0, Math.round(box[4] * oh));
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const x2 = Math.min(ow, Math.round(box[5] * ow));
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const y2 = Math.min(oh, Math.round(box[6] * oh));
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const faceBuf = await sharp(buffer)
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.extract({ left: x1, top: y1, width: x2 - x1, height: y2 - y1 })
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.resize(112, 112)
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.raw()
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.toBuffer();
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const floatData = new Float32Array(1 * 3 * 112 * 112);
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for (let i = 0; i < 112 * 112; i++) {
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floatData[0 * 12544 + i] = faceBuf[i * 3] / 255.0;
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floatData[1 * 12544 + i] = faceBuf[i * 3 + 1] / 255.0;
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floatData[2 * 12544 + i] = faceBuf[i * 3 + 2] / 255.0;
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}
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const tensor = new ort.Tensor('float32', floatData, [1, 3, 112, 112]);
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const sess = await getRecSession();
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const feeds = {}; feeds[sess.inputNames[0]] = tensor;
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const out = await sess.run(feeds);
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return out[sess.outputNames[0]].data;
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}
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function cosineSimilarity(a, b) {
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let dot = 0, na = 0, nb = 0;
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for (let i = 0; i < a.length; i++) {
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dot += a[i] * b[i];
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na += a[i] * a[i];
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nb += b[i] * b[i];
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}
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return dot / (Math.sqrt(na) * Math.sqrt(nb) + 1e-6);
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}
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router.post('/', requireAuth, async (req, res) => {
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const requestId = genReqId();
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const start = Date.now();
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try {
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const { image_url, image_base64, reference_image_url, reference_image_base64, user_id } = req.body || {};
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const img = await fetchImage({ image_url, image_base64 });
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const inputHash = hashInput(img.buffer);
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// Step 1: Detect face
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const face = await detectFace(img.buffer);
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if (!face) {
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return res.status(400).json({ ok: false, error: 'No face detected', request_id: requestId });
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}
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// Step 2: Liveness check
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const liveness = await checkLiveness(img.buffer, face.box);
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// Step 3: Compare with reference if provided
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let identityMatch = null;
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if (reference_image_url || reference_image_base64) {
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const refImg = await fetchImage({ image_url: reference_image_url, image_base64: reference_image_base64 });
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const refFace = await detectFace(refImg.buffer);
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if (refFace) {
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const emb1 = await getEmbedding(img.buffer, face.box);
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const emb2 = await getEmbedding(refImg.buffer, refFace.box);
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const sim = cosineSimilarity(emb1, emb2);
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identityMatch = { similarity: parseFloat(sim.toFixed(4)), match: sim > 0.6 };
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}
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}
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// Step 4: Risk scoring
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let riskScore = 0;
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if (liveness.label === 'spoof') riskScore += 0.8;
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else if (liveness.label === 'uncertain') riskScore += 0.4;
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if (identityMatch && !identityMatch.match) riskScore += 0.6;
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if (face.score < 0.7) riskScore += 0.2;
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riskScore = Math.min(1.0, riskScore);
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const confidence = face.score * (liveness.score || 0.5);
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const authenticated = liveness.label === 'live' && (!identityMatch || identityMatch.match);
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const result = {
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ok: true,
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endpoint: 'authenticate',
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request_id: requestId,
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user_id: user_id || null,
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authenticated,
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face: { detected: true, confidence: parseFloat(face.score.toFixed(4)) },
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liveness,
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identity_match: identityMatch,
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risk_score: parseFloat(riskScore.toFixed(4)),
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risk_level: riskScore > 0.7 ? 'high' : riskScore > 0.3 ? 'medium' : 'low',
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inference_time_ms: Date.now() - start,
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};
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await saveResult('authenticate', requestId, inputHash, result, confidence, { user_id, source: img.source });
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res.json(result);
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} catch (e) {
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console.error('[authenticate]', e);
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res.status(500).json({ ok: false, error: e.message, request_id: requestId });
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}
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});
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export default router;
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