Face recognition using early biologically inspired features
Biologically inspired model (BIM) is proven to be an effective feature representation approach for visual object categorization. In BIM, two successive S(simple)-to-C(complex) hierarchical layers are performed to simulate the visual perception process of primate visual cortex. However, the intensive computational cost above C1 layer in BIM extremely limits its application in real-time object recognition tasks. This paper proposes to use a set of improved early biologically inspired features (EBIF, including S1 and C1) for face recognition, in which pyramidal statistics of mean and standard deviation rather than MAX pooling are used for scale-tolerant feature condensation and local normalization is performed on C1 layer. Incremental PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) are then combined to efficiently learn a discriminant subspace for feature dimensionality reduction. In the matching stage, Cosine similarity is adopted as the distance metric for a given face pair. Experimental results on two public face datasets and a mobile face dataset show the effectiveness of the proposed method.