Global privacy concerns of facial recognition big data

Facial recognition technology is a system of automatic acknowledgement that recognizes individuals by categorizing specific features of their facial structure to link the scanned information to stored data. Within the past few decades facial recognition technology has been implemented on a large scale to increase the security measures needed to access personal information. This has been specifically used in surveillance systems, social media platforms, and mobile device access control. The extensive use of facial recognition systems has created challenges as it relates to biometric information control and privacy concerns. This concern raises the cost and benefit analysis of an individual’s security versus his/her privacy. Due to the contactless ability of facial recognition identification, the global market of this technology is expected to increase considerably over the next decade. This expansion implies the requirements of additional legal regulations in regard to the use of facial recognition technology. Data privacy laws have been passed in over 80 countries around the world and several states within the United States have created laws that apply to this form of technology. However increased action should be taken on a national level to enact stricter regulations in regard to biometric data collection and use

Balancing Biases and Preserving Privacy on Balanced Faces in the Wild

Demographic biases exist in current models used for facial recognition (FR). Our Balanced Faces in the Wild (BFW) dataset is a proxy to measure bias across ethnicity and gender subgroups, allowing one to characterize FR performances per subgroup. We show that results are non-optimal when a single score threshold determines whether sample pairs are genuine or imposters. Furthermore, within subgroups, performance often varies significantly from the global average. Thus, specific error rates only hold for populations matching the validation data. We mitigate the imbalanced performances using a novel domain adaptation learning scheme on the facial features extracted from state-of-the-art neural networks, boosting the average performance. The proposed method also preserves identity information while removing demographic knowledge. The removal of demographic knowledge prevents potential biases from being injected into decision-making and protects privacy since demographic information is no longer available. We explore the proposed method and show that subgroup classifiers can no longer learn from the features projected using our domain adaptation scheme. For source code and data, see https://github.com/visionjo/facerec-bias-bfw.Comment: arXiv admin note: text overlap with arXiv:2102.0894

Cancellable face template algorithm based on speeded-up robust features and winner-takes-all

Features such as face, fingerprint, and iris imprints have been used for authentication in biometric system. The toughest feature amongst these is the face. Extracting a region with the most potential face features from an image for biometric identification followed by illumination enhancement is a commonly used method. However, the region of interest extraction followed by illumination enhancement is sensitive to image face feature displacement, skewed image, and bad illumination. This research presents a cancell able face image algorithm built upon the speeded-up robust features method to extract and select features. A speeded-up robust feature approach is utilised for the image’s features extraction, while Winner-Takes-All hashing is utilised for match-seeking. Finally, the features vectors are projected by utilising a random form of binary orthogonal matrice. Experiments were conducted on Yale and ORL datasets which provide gray scale images of sizes 168 × 192 and 112 × 92 pixels, respectively. The execution of the proposed algorithm was measured against several algorithms using equal error rate metric. It is found that the proposed algorithm produced an acceptable performance which indicates that this algorithm can be used in biometric security applications