On the complex domain deep machine learning for face recognition

Biometric based verification and recognition has become the center of attention for many significant applications for security conscious societies, as it is believed that biometrics can provide accurate and reliable identification. The face biometrics are one that possesses the merits of both high accuracy and low intrusiveness. An efficient machine recognition of human faces in big dataset is both important and challenging tasks. This paper addresses an intelligent face recognition system that is pose invariant and can recognize multi-expression, occluded and blurred faces through efficient but compact deep learning. Superior functionality of neural network in a complex domain has been observed in recent researches. My work presents a new approach, which is the fusion of higher-order novel neuron models with multivariate statistical techniques in a complex domain with a sole goal of improving performance of biometric systems. This also aims at reducing the computational cost and providing a faster recognition system. This paper presents the formal algorithms for feature extraction with multivariate statistical techniques in complex domain and compare them their real domain counterpart. This paper also presents a classifier structure (OCON : One-Class-in-One-Neuron) which contains an ensemble of novel higher order neurons, which drastically reduces the complexity of proposed learning machine because only single neuron is sufficient to recognize a subject in the database. This novel fusion in the proposed deep learning machine has thoroughly presented its superiority over a wide spectrum of experiments. Advanced deep learning capabilities, and complex domain implementation in particular, are significantly advancing state-of-art in computer vision and pattern recognition.