Learning Representations for Cryptographic Hash Based Face Template Protection

Abstract

In this chapter, we discuss the impact of recent advancements in deep learning in the field of biometric template protection. The representation learning ability of neural networks has enabled them to achieve state-of-the-art results in several fields, including face recognition. Consequently, biometric authentication using facial images has also benefited from this, with deep convolutional neural networks pushing the matching performance numbers to all time highs. This chapter studies the ability of neural networks to learn representations which could benefit template security in addition to matching accuracy. Cryptographic hashing is generally considered most secure form of protection for the biometric data, but comes at the high cost of requiring an exact match between the enrollment and verification templates. This requirement generally leads to a severe loss in matching performance (FAR and FRR) of the system. We focus on two relatively recent face template protection algorithms that study the suitability of representations learned by neural networks for cryptographic hash based template protection. Local region hashing tackles hash-based template security by attempting exact matches between features extracted from local regions of the face as opposed to the entire face. A comparison of the suitability of different feature extractors for the task is presented and it is found that a representation learned by an autoencoder is the most promising. Deep secure encoding tackles the problem in an alternative way by learning a robust mapping of face classes to secure codes which are then hashed and stored as the secure template. This approach overcomes several pitfalls of local region hashing and other face template algorithms. It also achieves state-of-the-art matching performance with a high standard of template security.