Efficient Face Re-Identification through PSO Based Adaptive Deep Learning Models

Abstract

Face plays a vital role in the Recognition or Re-Identification of a person. Therefore, it is significant to identify and extract the facial visual features that automatically lead to face identification-based classification. Facial features comprise different ways of detection, for instance, they could be located at corners or midpoints of the facial features that rely on multiple components such as eyes, lips, nose with different emotions and expressions used in face recognition. This paper introduced a robust and efficient deep learning model with the use of a transfer learning approach for PSO for extraction and selection of the best facial features. Deep learning models “Openface via PSO and introduced customized Inception-V3 model via PSO is used and present detailed comparative accuracy of both models in terms of classification recognition. For this, the paper presents seven different algorithms to evaluate the efficiency of the model with four different face databases. It is evident from the result; neural network classifier shows a gradual hike to calculate accuracy with the proposed PSO-based OpenFace deep learning approach. On the other hand, random forest and AdaBoost algorithm were observed most compatible with the customized PSO-based Inception-V3 model.