Univrsity Of Tehran Press Journal of Information Technology Management 2980-7972 18 1 2026 03 01 An Intelligent Hybrid Feature-Engineering Approach for Covariant Face Recognition Using Deep Learning 102 122 106256 10.22059/jitm.2026.106256 EN Pavitha U S Research Scholar (Part Time), Research Centre - Department of Electronics and Communication Engineering, M. S. Ramaiah Institute of Technology, Bengaluru, India; Assistant Professor, Department of Electronics and Communication Engineering - M. S. Ramaiah Institute of Technology, Bengaluru, India. Suma K V Associate Professor, Department of Electronics and Communication Engineering, M. S. Ramaiah Institute of Technology, Bengaluru, India; Affiliated to Visvesvaraya Technological University, Belagavi-590018, Karnataka, India. Journal Article 2026 03 06 Face recognition (FR) is a non-contact biometric method integral to national and social security. It is pivotal in sectors like security, healthcare, banking, and criminal identification. Various techniques are being developed, including appearance and hybrid approaches, which either target specific facial features or consider the whole face for effective image recognition. This study explores hybrid machine learning techniques and enhanced covariates related to FR. The suggested approach is examined from a number of input viewpoints, including illumination, position variation, facial emotions, occlusions, and aging, which resulted in the widespread use of FR systems. The Generative Adversarial Network (GAN) is used to multiply the image on the dataset for image augmentation purposes. The facial covariants are extracted with the help of a hybrid feature engineering technique, such as a voting classifier that includes algorithms like K Nearest Neighbour, Support Vector Machine, and Random Forest Algorithm (KNN-SVM-RF). The dimension redundancy is achieved with the help of a combination of Principal Component Analysis and Independent Component Analysis algorithms. The modified VGG-16 algorithm is used to predict the image with the covariant similarity percentage of a person. Experiments conducted on the CelebFaces Attributes (CelebA) Dataset and Celebrity Face Image Dataset demonstrate that the hybrid strategy yields superior accuracy and robustness compared with CNN-only or classical approaches, achieving notable improvements in Precision, Recall, F1 score, and overall Accuracy. https://jitm.ut.ac.ir/article_106256_349d9d29e312c12e24179176a5f899e2.pdf