CoRAE: Energy Compaction-Based Correlation Pattern Recognition Training Using AutoEncoder

Automatic Target Recognition (ATR) using Correlation Pattern Recognition (CPR) in IoT-based applications encounters limitations like limited memory and inadequate computational resources. One reason is the required quantity of reference templates for each target/object to cover all features of a target/object. To mitigate the issue of reference templates per target/object without accuracy degradation, this paper proposes energy compaction-based CPR autoencoder-training. Additionally, a newly proposed performance metric known as Peak Energy Gain (PEG) estimates the quality of the correlation plane and the feature compression capability CPR methods. The proposed, composite filtering strategy, Eigen Maximum Average Correlation Height (EMACH), and Extended Eigen Maximum Average Correlation Height ( $E^{2}$ MACH) are vigorously validated using publicly available biometric and object image databases. By training a single reference template, the proposed training method achieves 97.97% mean accuracy with the second-best approach of $E^{2}$ MACH that attains 53.04% mean accuracy on the Pose Estimation Database. For bio-metric fingerprint verification, the mean Equal Error Rate (EER) of the proposed approach and the composite strategy is 3% and 29.69%, respectively on the FVC2002DB1A database. Similarly, the mean EER of the proposed approach and the composite strategy is 10.55% and 26.32%, respectively on the FVC2006IA database. For FEI faces dataset, the proposed method achieves 1.41% mean EER, and the composite filtering approach achieves 21.43% mean EER. On the University of Tehran Iris database, the proposed autoencoder-based methodology obtains 19.07%, and 18.07% mean EER on the left and right side iris instances, respectively. The comparative results for each dataset demonstrate superiority of AE-based method over the state-of-the-art CPR methods.