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This paper proposes an Adaptive Fuzzy Classifier Approach (AFCA) to local edge detection in order to address the challenges of detecting latent fingerprint in severely degraded images. The proposed approach adapts classifier parameters to different parts of input images using the concept of reference neighborhood. Three variants of AFCAs, namely K-means-clustering AFCA, Entropy-based AFCA, and Statistical AFCA, were developed. Experiments were conducted both on synthetic images and on real fingerprint images to compare these AFCAs and Canny edge detection. The presented results show that Statistical AFCA is the best performer with latent images.