On the Use of Synthetic Hand Images for Biometric Recognition
Robert Nichols, Lazaro Janier Gonzalez-Soler, Christian Rathgeb · 2024
Recognition of subjects based on images of their hands does not enjoy the same level of publicity as e.g. face recognition; therefore, scientific studies on this topic are limited. Nevertheless, the importance in forensic scenarios is considerable, where investigators often face the difficult task of identifying a suspect with little more than an image depicting a partial hand, i.e. the palmar or dorsal aspect. However, the large amounts of data needed for robust recognition systems are often unavailable. Recent advancements in the area of generative artificial intelligence have demonstrated impressive capabilities in terms of image fidelity and performance, in turn implying the possibility of substituting or augmenting real datasets with synthetically generated samples. In this paper, we explore generating hand images with latent diffusion models (LDM) conditioned on state-of-the-art hand recognition systems. Our experimental results indicate the possible future viability of generating fully synthetic identity-preserving mated samples. We identify interesting behaviour of the involved algorithms to encourage future work in this area and ultimately facilitate the development of robust, privacy-preserving and unbiased biometric systems.