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face-recognition

face recognition and unlock

Face ID

SystemTechniques · 4Primitives · 24 face-recognition Face embeddingsmachine-learning · medium: Face recognition embeddings trained with margin softmax (ArcFace, CosFace) and threshold-based verificationLiveness detectionmachine-learning · medium: Presentation attack detection for faces: challenge-response, depth/texture cues, replay detectionObject detectionmachine-learning · medium: A network turns an image into scored, classified boxes in one pass; anchor-based, anchor-free and set-prediction heads differ in how they assign labels and remove duplicates.›HNSWsearch-sort · medium: Hierarchical Navigable Small World: a layered proximity graph searched greedily from a sparse top layer; the default in-memory approximate nearest-neighbour index.›Facial landmark alignmentmachine-learning · low: Detecting facial keypoints and warping faces to a canonical pose before embeddingLoss functionsmachine-learning · low: The scalar a model minimizes: cross-entropy for classes and tokens, MSE or Huber for values, each with a stable form and simple gradient.›Convolutional networksml-training · low: Layers that slide a small shared kernel over a grid, giving translation equivariance and few parameters; still the most compute-efficient choice for many vision tasks.›Calibrationmachine-learning · low: Make predicted probabilities match observed frequencies: measure with ECE, fix post hoc with temperature, Platt or isotonic scaling on held-out data.›Evaluation metricsmachine-learning · low: Precision, recall, ROC AUC, average precision, log loss, NDCG and pass@k computed correctly, plus paired confidence intervals for comparing models.›Weight quantizationml-training · low: Store weights as 8/4-bit integers with per-group scales; decode is memory-bound, so 4-bit weights give about 3-4x tokens/s and fit 4x larger models.›Structured light depthrobotics · low: Depth from projected dot patterns or time-of-flight for 3D face captureAEAD (AES-GCM, ChaCha20-Poly1305)security · low: Authenticated encryption that encrypts and detects tampering in one call; the only kind of symmetric encryption you should use.›Hardware backed keystoresecurity · low: Non-exportable keys in Secure Enclave, StrongBox, TPM: generation, use policies, biometric gatingTrusted execution environmentssecurity · low: Enclaves/confidential VMs with remote attestation for computing on sealed dataNormalization layersmachine-learning · low: Standardize activations per batch, token or group with a learned scale so deep nets train fast and stably; RMSNorm in LLMs, BatchNorm in convnets.›Data augmentationmachine-learning · low: Generate label-preserving variants of each training example on the fly to enlarge the effective dataset and bake in invariances.›Non-maximum suppressionmachine-learning · low: Keep the best-scoring candidate in each neighbourhood and drop the rest; greedy NMS, Soft-NMS and 1-D peak picking share one loop.›d-ary heapdata-structures · low: Array heap with d children per node (d = 4 typical): half the binary heap's depth, siblings in one cache line, faster priority queues.›SIMD programminghardware-os · low: Process 4-16 lanes per instruction with AVX2, AVX-512 or NEON via Rust std::arch, with runtime dispatch and a portable fallback.›PRNGs (PCG, xoshiro)numerics · low: Small, fast, statistically strong non-cryptographic generators: PCG32 and xoshiro256**, with seeding, parallel streams and unbiased ranges.›+ 8 more primitives

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