- Introduces hybrid deep learning framework combining deep features from top-performing pre-trained CNNs via feature-level fusion, smoothing, flattening and concatenation for classification.
- Evaluated eight pre-trained CNN architectures and selected the four most effective models for subsequent feature fusion and classification.
- Demonstrates superior performance over individual models: 96.9% accuracy, 94.1% precision, 100% recall, and 96.9% F1-score.
J Vis Exp. 2026 Oct 16;(236). doi: 10.3791/72192.
ABSTRACT
The performance of many facial recognition (FR) algorithms is adversely affected in uncontrolled settings due to diverse facial expressions, lighting changes, occlusions, and image noise. This poses a significant challenge in developing a reliable and accurate facial recognition system for real-world applications. Hence, this paper introduces a novel hybrid deep learning-based facial recognition framework aimed at overcoming the shortcomings of the current solutions, specifically, inaccuracy and poor computational efficiency caused by complex facial images. Several pre-trained CNN architectures (VGG16, EfficientNetB0, ResNet-50, MobileNet, MobileNetV2, Xception, InceptionResNetV2, and NASNetLarge) are utilized in the proposed solution. Eight CNN models were evaluated, and the four most effective were identified based on their performance metrics. The proposed hybrid system employs a feature-level fusion method in which deep features from the best-performing models are combined. Subsequently, a smoothing operation on the resultant feature vector was performed, and the output vector was flattened and concatenated for the classification stage. According to the experimental results, the proposed hybrid framework outperforms all the individual CNN models used in the study, achieving an accuracy rate of 96.9%, a precision of 94.1%, a recall of 100%, and an F1-score of 96.9%.
PMID:42891605 | DOI:10.3791/72192
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