A local multiple patterns feature descriptor for face recognition

Abstract Human perception of a signal depends on the ratio of the change of stimulus to the stimulus itself while the change of stimulus to the stimulus itself is usually ignored in hand-crafted feature descriptors. However, it is important for extracting discriminant feature. To address this problem, we firstly develop a local multiple patterns (LMP) feature descriptor based on the Weber's law for feature extraction and face recognition. In LMP, (1) the Weber's ratio is modified to contain the change direction, and thus the modified Weber's ratio is quantized into several intervals to generate multiple feature maps for describing different changes; (2) LMP concatenates the histograms of the non-overlapping regions of the feature maps for image representation. Secondly, since LMP could only capture small-scale structures, a multi-scale block LMP (MB-LMP) is presented to generate more discriminative and robust visual feature. Thus, MB-LMP could capture both the small-scale and large-scale structures. In implementation, MB-LMP is computationally efficient using integral image. In experiments, LMP and MB-LMP are evaluated on four public datasets for face recognition. The experimental results demonstrate the promise of the proposed LMP and MB-LMP descriptors with desirable efficiency.