Mixed neighborhood topology cross decoded patterns for image-based face recognition. (30th December 2018) - Kas, M.; El merabet, Y.; Ruichek, Y.; Messoussi, R.
Highlights: Proposition of a new LBP like descriptor for face recognition referred to as MNTCDP. Evaluating the proposed method on ORL, FERET, YALE, Extended Yale B and AR databases. Providing a fair comparison between the proposed MNTCDP descriptor and 51 LBP variants. The proposed MNTCDP based system outperforms several recent state-of-the-art systems. The performance of MNTCDP descriptor is compared to the one of deep learning methods. Abstract: Face recognition becomes an important task performed routinely in our daily lives. This application is encouraged by the wide availability of powerful and low-cost desktop and embedded computing systems, while the need comes from the integration in too much real world systems including biometric authentication, surveillance, human-computer interaction, and multimedia management. Moreover, face recognition technology is now adopted in new intelligent systems and devices like smart-phones, which impose some constraints related to the complexity and execution time of the recognition process. This fact brings new challenges and gives much more area to extend the ongoing researches. This research field experienced the development of many methods and architectures aiming at producing face recognition systems which are efficient in terms of precision, robustness and computation time. In the same context, this article proposes a new feature descriptor referred to as Mixed Neighborhood Topology Cross Decoded Patterns (MNTCDP) as an effective face descriptor, The proposed handcrafted descriptor fulfills the needs of current face recognition applications and can be integrated in different platforms, requiring simple, robust and computationally low algorithms. Instead of heuristic code constructions, MNTCDP is built using new neighborhood topology and new pattern encoding scheme, which have high ability to extract discriminative and stable face representation. The adopted face recognition system consists of three stages: (1) face detection and alignment to normalize the input images to a common form if needed; (2) feature extraction using the proposed MNTCDP descriptor and (3) face recognition through a supervised image classification task using the simple K-Nearest Neighbors classifier. Simulated experiments on ORL, YALE, Extended Yale B, FERET and AR datasets acquired under different illumination conditions or facial expressions show that the proposed MNTCDP descriptor presents high performance ability in classifying face images. MNTCDP demonstrates superior performance than a large number of recent state-of-the-art LBP variants and deep learning methods, as well as recent most promising works of the literature.