Shahrel Azmin Suandi

By Nuzrul Fahmi Nordin, Samsul Setumin, Abduljalil Radman and Shahrel Azmin Suandi

An excellent face recognition for a surveillance camera system requires remarkable and robust face descriptor. Binary gradient pattern (BGP) descriptor is one of the ideal descriptors for facial feature extraction. However, exploiting local features merely from smaller region or microstructure does not capture a complete facial feature. In this paper, an extended binary gradient pattern (eBGP) is proposed to capture both micro- and macrostructure information of a local region to boost up the descriptor performance and discriminative power. Two topologies, the patch-based and circular-based topologies, are incorporated with the eBGP to test its robustness against illumination, image quality, and uncontrolled capture conditions using the SCface database. Experimental results show that the fusion between micro- and macrostructure information significantly boosts up the descriptor performance. It also illustrates that the proposed eBGP descriptor outperforms the conventional BGP on both the patch-based topology and the circular-based topology. Furthermore, a fusion of information from two different image types, orientational image gradient magnitude (OIGM) and grayscale image, attained better performance than using OIGM image only. The overall results indicate that the proposed eBGP descriptor improves the recognition performance with respect to the baseline BGP descriptor.

Part of the book: Visual Object Tracking with Deep Neural Networks

By Hussein Samma and Shahrel Azmin Suandi

Machine learning experts expected that transfer learning will be the next research frontier. Indeed, in the era of deep learning and big data, there are many powerful pre-trained CNN models that have been deployed. Therefore, using the concept of transfer learning, these pre-trained CNN models could be re-trained to tackle a new pattern recognition problem. As such, this work is aiming to investigate the application of transferred VGG19-based CNN model to solve the problem of fingerprint liveness recognition. In particular, the transferred VGG19-based CNN model will be modified, re-trained, and finely tuned to recognize real and fake fingerprint images. Moreover, different architecture of the transferred VGG19-based CNN model has examined including shallow model, medium model, and deep model. To assess the performances of each architecture, LivDet2009 database was employed. Reported results indicated that the best recognition rate was achieved from shallow VGG19-based CNN model with 92% accuracy.

Part of the book: Biometric Systems