The Application of Improved Scale Invariant Feature Transformation Algorithm in Facial Recognition

Abstract

Currently, face recognition models suffer from insufficient accuracy, stability, and computational efficiency. To address this issue, an improved feature extraction algorithm on the ground of Haar wavelet features and scale invariant feature transformation algorithm is proposed. In addition, the study also combines this algorithm with deep belief networks to construct an improved facial recognition model. The effectiveness of the proposed improved feature extraction algorithm was verified, and it was found that the recognition accuracy of the algorithm was 94.2%, which is better than other comparative algorithms. In addition, the study also conducted empirical analysis on the improved facial recognition model and found that the recognition accuracy of the model was 0.92, and the feature matching time was 2.6 seconds, which was better than other comparative models in terms of performance. On the ground of the above results, the proposed facial recognition model has significantly improved recognition accuracy and efficiency compared to traditional models. It can provide theoretical reference for improving the universality of facial recognition applications in different fields.