Criminal Identification System to Improve Accuracy of Face Recognition using Innovative CNN in Comparison with HAAR Cascade

Abstract

Aim: Criminal face identification system helps in identifying criminal or suspect and also retrieves information about the suspect. This saves us time for manual work. Innovative Convolutional Neural Network (CNN) are applied to discriminate between criminal and non-criminal facial images.

Materials and Methods: Criminal face recognition is performed using CNN (N=10) over HAAR Cascade (N=10) was iterated 10 times for efficient and accurate analysis based on labeled data with 2 groups G power in 80% and threshold 0.05%, CI 95% mean and standard deviation. The split size of training and testing of 70% and 30% respectively.

Results: After analyzing the results, the accuracy for HAAR is 84.50% and the accuracy for CNN is 90.30% and attained the significance value of p=0.0309 (p<0.05), showing that there is a significant difference between the groups.

Conclusion: CNN is better than the HAAR Cascade algorithm for face recognition.