Optimization of Facial Images to Predict Gender Using HDSON and Bidirectional Associative Memory

Abstract

Objective: To enhance the Gender prophecy by employing facial images using imaginative algorithm and to resolve real time applications. Method: Initially, we make use of shared deep octonion network and the Octonion-Valued Neural Network (OVNN) to develop a generic framework for a Hybrid Deep Sparse Octonion Network (HDSON). Sparse Coding Octonion data Algorithm (SCOA) is exploited to depict the face images up to seven color channels and improves the weight of HDSON. Furthermore, to take advantage of the maximum storage we make use of Bidirectional Associative Memories (BAM). Findings: The proposed approach resolves both the issues of depiction of the facial image and its storage, since the present study combines the characteristics of SCOA to improve HDSON weight and BAM to enhance the storage. Moreover, the present study is simple to apply and effective in real time applications. Novelty: The proposed approach can be used in paramilitary to minimize cross border terrorism; in addition, the presented scheme can enhance the probability of child detection and may help local police to a large extent. Keywords: Automatic Gender Classification; BAM; DCN; HDSON; Octonion; SCOA; OVNN