A novel Quantized Gradient Direction based face image representation and recognition technique
Manisha Parlewar, Hemprasad Yashwant Patil, Kishor M. Bhurchandi · 2016 Twenty Second National Conference on Communication (NCC) · 2016
This paper presents a novel quantized gradient based local feature descriptor, named Local Quantized Gradient Direction (LQGD) descriptor and the subsequent Partitioned Gradient Histogram, for facial image representation. The 8 bit LQGD descriptor accommodates eight levels quantized gradient magnitude and direction information from the horizontal and vertical gradients at local facial image pixels using 3×3 neighborhoods. The subsequent novel partitioned histogram based feature detection using the proposed descriptor offers separation in feature space resulting in recognition performance improvement. The technique is also robust to rotation, scale variations and noise due to typical preprocessing, background minimization and the descriptor itself. Spatial and transform domain feature level fusion is used for further performance improvement. The benchmarking of the proposed technique has been done using publicly available YEL and JAFFE databases with other contemporary techniques. The proposed technique outperforms the other published contemporary techniques.