Learning the hash code with generalised regression neural networks for handwritten signature biometric data retrieval

Handwritten signature recognition is one important component of biometric authentication. This is a central process in a broad range of areas requiring personal identification, such as security, legal contracts and bank transactions. Extensive efforts have been put into the research towards the verification of handwritten signatures, which contain biometric information. Although many successful methods have been used, they often disregard the size of databases, which can be very large, posing scalability problems to their application in real-world scenarios. To overcome this problem, in this paper, we use binary embeddings of high-dimensional data which is an efficient tool for indexing big datasets of biometric images. The rationale is to find a good hash function such that similar data points in Euclidean space preserve their similarities in the resulting Hamming space for fast data retrieval and state-of-the-art classification performance. In the settings of an handwritten signature retrieval system, an indexing hashing-based scheme is presented. We propose to learn k-bits hash code with a generalised regression neural network (GRNN), which yielded competitive results in the GPDS database.