IPM-Model: AI and metaheuristic-enabled face recognition using image partial matching for multimedia forensics investigation with genetic algorithm

• Published in: Multimedia tools and applications Vol. 81; no. 17; pp. 23533 - 23549
• Main Authors: Khan, Abdullah Ayub, Shaikh, Aftab Ahmed, Shaikh, Zaffar Ahmed, Laghari, Asif Ali, Karim, Shahid
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2022; Springer Nature B.V
• Subjects: Artificial intelligence; Color imagery; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Datasets; Face recognition; Forensic computing; Forensic sciences; Genetic algorithms; Heuristic methods; Information technology; Matching; Multimedia; Multimedia Information Systems; Neural networks; Object recognition; Pattern recognition; Principal components analysis; Special Purpose and Application-Based Systems; Artificial Intelligence (AI); Metaheuristic; Image Processing and Recognition; Digital Forensics Investigation; Artificial neural networks (ANN); Principal component analysis (PCA); Partial image matching
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-022-12398-x

The rapid enhancement in the development of information technology has driven the development of human facial image recognition. Recently, facial recognition has been successfully applied in several distinct domains with the help of computing and information technology. This kind of application plays a significant role in the process of digital forensics investigation, recognizing the patterns of a human face based on the partial matching of images that would be in 24-bit color image format, including the spacing of the eyes, the bridging of the nose, the contour of the lips, ears, and chin. In this paper, we have proposed and implemented an image recognition model based on principal component analysis, genetic algorithms, and neural networks, in which PCA reduces the dimension of the benchmark dataset, while genetic algorithms and neural nets optimize the searching patterns of image matching and provide highly efficient output with a minimal amount of time. Through the experiment results on the human facial images dataset of the Georgia Institute of Technology, the overall match showed that the proposed model can achieve the recognition of human face images with an accuracy rate of 93.7%. Moreover, this model helps to examine, analyze, and detect individuals by partial matching with reidentification in the procedure of forensics investigation. The experimental result shows the robustness of the proposed model in terms of efficiency compared to other state-of-the-art methods.