Enhancing face recognition privacy through the integration of differential privacy and convolutional neural network

Protecting facial recognition privacy is crucial amid deep fake threats, biometric risks, and third-party database access concerns. Despite many recent face recognition methods achieving high accuracy, most existing works either ignore privacy protection or apply privacy mechanisms without designing CNN structures that effectively learn from heavily perturbed facial data. This research introduces a secure face recognition system based on Differential Privacy (DP), employing a Convolutional Neural Network (CNN) and face classifiers. In this study, we develop a CNN through the incorporation of multiple batch normalization layers. This CNN is capable of recognizing the randomized image of the DP technique. To ensure privacy, the face database undergoes perturbation using DP techniques before releasing to any unauthorized access. The CNN model learns from these perturbed images, extracting features that are subsequently used by a predictor to classify the face. The CNN model learns from these images and then this trained CNN extracts features from an image that needs to be recognized. Ultimately, a predictor classifies this face. We evaluate three DP techniques namely Laplacian, Gaussian, and DP-blur using four predictors to evaluate the privacy-preserving capabilities of the proposed method. Each DP technique is evaluated by varying privacy parameters from 0.5 to 8 with an interval of 0.5. This research employs two datasets, namely LFW and IC. The DP blur with Logistic regression predictors provides the highest privacy, achieving excellent accuracy rates of 97% and 77% for these datasets. This outcome surpasses all baseline methods. The research offers an in-depth analysis of various DP techniques to construct a secure face recognition system. The method will aid in the automatic recognition of faces while ensuring privacy.