Edge Detection for Pattern Recognition: A Survey <s> Biometric recognition <s> Edge detection algorithms often produce broken edges, especially in noisy images. We propose an algorithm based on discrete particle swarm optimisation (PSO) to detect continuous edges in noisy images. A constrained PSO-based algorithm with a new objective function is proposed to address noise and reduce broken edges. The localisation accuracy of the new algorithm is compared with that of a modified version of the Canny algorithm as a Gaussian-based edge detector, the robust rank order (RRO)-based algorithm as a statistical based edge detector, and our previously developed PSO-based algorithm. Pratt's figure of merit is used as a measure of localisation accuracy for these edge detection algorithms. Experimental results show that the performance of the new algorithm is higher than the Canny and RRO algorithms in the images corrupted by two different types of noise (impulsive and Gaussian noise). The new algorithm also detects edges more accurately and smoothly than our previously developed algorithm in noisy images. <s> BIB001 </s> Edge Detection for Pattern Recognition: A Survey <s> Biometric recognition <s> The inability of automated edge detection methods inspired from primal sketch models to accurately calculate object edges under the influence of pixel noise is an open problem. Extending the principles of image perception i.e. Weber–Fechner law, and Sheperd similarity law, we propose a new edge detection method and formulation that use perceived brightness and neighbourhood similarity calculations in the determination of robust object edges. The robustness of the detected edges is benchmark against Sobel, SIS, Kirsch, and Prewitt edge detection methods in an example face recognition problem showing statistically significant improvement in recognition accuracy and pixel noise tolerance. <s> BIB002
Face images contain distinct biometric information from the boundaries of eyes, nose, mouth, and jaw shape, that can be represented with image edges (Rodrigues & du Buf, 2009; Yang et al., 1996 . The features inspired from the spatial change detection (Alex Pappachen  ) that results in feature edges have shown high robustness to natural variabilities (Alex P. James, 2013; Pappachen James, James, & Sima, 2010) . The noise that usually makes the feature extraction of the edges extremely difficult task would lead to poor performance of face recognition (Alex et al., 2012, 2013; R. Hu et al., 2013; Mathew & James, 2015 . The use of psychometric measures in the calculation of edges have shown to improve the robustness to the face recognition BIB002 . BIB001 