Local symmetric directional pattern: A novel descriptor for extracting compact and distinctive features in face recognition

This paper presents a novel approach in feature extraction, Local Symmetric Directional Pattern (LSDP), for face recognition. The LSDP encodes the structure of facial textures based on gradient information in a simple and compact coding approach to produce more distinctive code in less time and memory than existing methods. We extract gradient information by convolving the face image with four symmetric compass masks to encode this information using directional numbers, which are related to directional information, and magnitudes of the two prominent edge responses. We also use a hybrid feature vector as a face descriptor obtained by reducing the dimensions of the LSDP feature map and classify them using the sparse representation-based classification (SRC) algorithm. Due to the high discrimination power of the extracted features, the construction of a dictionary based on the hybrid features leads to more sparse representation coefficients. As a result, it improves SRC performance in terms of recognition rate and computational speed. We perform extensive experiments to evaluate and compare the performance of our descriptor with other handcrafted descriptors and the deep learning feature under two different evaluation protocols (different dimensions of feature space and the different number of training samples) on different face databases, which have different variations of illumination, expression, and pose. Experimental results illustrate that our method achieves the highest recognition rate compared to other methods in both evaluation protocols. Especially under challenging conditions where the dimensions of the feature space or the number of training samples are low, LSDP demonstrates excellent performance.

Automated summary

This paper introduces a novel approach called LSDP for face recognition, which encodes facial textures based on gradient information in a simple and compact way. Experimental results demonstrate that LSDP achieves higher recognition rates compared to other methods under different evaluation protocols, especially in challenging conditions with low dimensions of the feature space or fewer training samples.