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Research on Face Recognition based on Edge Alignment and Image Fusion

Image fusion technology has been widely applied in the field of face recognition in recent years. Most scholars focus on the improvement and innovation of fusion methods to optimize the performance of face recognition. However, face localization before image fusion also determines the quality of fusion images and recognition performance. To further improve the quality of the fused images, this paper performed Canny edge alignment on the thermal images and visible images in the Tufts Face Database, located and cut out the face part according to the projection spectrum. Then, several traditional fusion techniques, such as Weighted Average technique and Select Minimum technique, were used to fuse the two images. Finally, the IR3C algorithm was used to calculate the recognition accuracy of each fusion method. The final results showed that using Canny edge detection method and pixel projection method could achieve high-quality face localization and segmentation, and further improve the quality of the fusion image and the accuracy of face recognition, especially the Weighted Average technique and Select Minimum technique showed 100% recognition accuracy.