Local Directional Gradient Pattern: A Local Descriptor for Face Recognition
This is an incremental improvement for face recognition systems, offering faster processing with comparable accuracy.
The paper tackles face recognition by proposing a local directional gradient pattern (LDGP) descriptor that reduces extraction and matching time while maintaining recognition rates similar to state-of-the-art methods, as shown in experiments on AT&T, Extended Yale B, and CMU-PIE databases.
In this paper a local pattern descriptor in high order derivative space is proposed for face recognition. The proposed local directional gradient pattern (LDGP) is a 1D local micropattern computed by encoding the relationships between the higher order derivatives of the reference pixel in four distinct directions. The proposed descriptor identifies the relationship between the high order derivatives of the referenced pixel in four different directions to compute the micropattern which corresponds to the local feature. Proposed descriptor considerably reduces the length of the micropattern which consequently reduces the extraction time and matching time while maintaining the recognition rate. Results of the extensive experiments conducted on benchmark databases AT&T, Extended Yale B and CMU-PIE show that the proposed descriptor significantly reduces the extraction as well as matching time while the recognition rate is almost similar to the existing state of the art methods.