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Face Verification Using the LARK Representation
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AI
DOI：10.1109/TIFS.2011.2159205.png)
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Abstract
En 中文
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.
Keywords:
Face verification
labeled faces in the wild (LFW)
locally adaptive regression kernels (LARKs)
matrix cosine similarity
one-shot similarity (OSS)
Journal
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8
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5.2K
Citations:
2.3W
