Abstract: Reliable human recognition still remains a challenging task from a security perspective. Changes in illumination limit a Unimodal Biometric Identification system that uses a single biometric modality, pose variations, occlusions, and behavioral variations. To overcome the limitations mentioned above, we propose a multi-biometric approach combining face and gait recognition. In this research, we propose an adaptive multibiometric identification system that combines face and gait information via a hybrid deep feature fusion, thereby enhancing security. To identify a face, we first locally enhance the face using a Gabor filter to capture local face information and then extract deep features using ResNet50-based deep learning architecture. The extracted features are pruned using Principal Component Analysis (PCA). On the other hand, Gait Energy Images (GEI) are used to present a gait sequence, and a convolutional neural network with a bidirectional long short-term memory (CNN-BiLSTM) is employed to capture significant gait features. PCA is applied to remove feature redundancies. After that, both face and gait features are combined using an adaptive attention model that weights the relative importance of each modality, and final classification is performed with a Softmax classifier. It is demonstrated that our final model converges well within a limited number of training iterations, achieving very high training accuracy and good validation accuracy, with clear class separability as observed in the confusion matrix. We also evaluate the performance of our final system using biometric performance curves (ROC), FAR, FRR, EER, and Grad-CAM visualization to assess whether the model is capturing the important biometric regions. Our proposed model outperforms both traditional unimodal and multimodal biometric identification methods. The proposed system can be used to securely identify humans for surveillance and authentication within and across organizations. The proposed model provides a balanced trade-off between accuracy, robustness, and interpretability in multimodal biometric recognition.

Key words: multibiometric identification, attention fusion, gait energy images, Gabor filter, PCA