Abstract-We present a novel face representation based on locally adaptive regression kernel (LARK) descriptors. Our LARK descriptor measures a self-similarity based on "signal-induced distance" between a center pixel and surrounding pixels in a local neighborhood. By applying principal component analysis (PCA) and a logistic function to LARK consecutively, we develop a new binary-like face representation which achieves state-of-the-art face verification performance on the challenging benchmark "Labeled Faces in the Wild" (LFW) dataset. In the case where training data are available, we employ one-shot similarity (OSS) based on linear discriminant analysis (LDA). The proposed approach achieves state-of-the-art performance on both the unsupervised setting and the image restrictive training setting (72.23% and 78.90% verification rates), respectively, as a single descriptor representation, with no preprocessing step. As opposed to combined 30 distances which achieve 85.13%, we achieve comparable performance (85.1%) with only 14 distances while significantly reducing computational complexity. Index Terms-Face verification, labeled faces in the wild (LFW), locally adaptive regression kernels (LARKs), matrix cosine similarity, one-shot similarity (OSS).
In another training-free approach, locally adaptive regression kernels (LARK) REF were employed as visual descriptors, in conjunction with the matrix cosine similarity (MCS) measure.
Face Verification Using the LARK Representation
IEEE Transactions on Information Forensics and Security
IEEE Transactions on Information Forensics and Security
Computer Science