Multi-task Extreme Learning Machine for Palm Vein Multiclassification

Using biometric systems for individual identification is an alternative that provides excellent data security since this technology uses unique and distinctive information about a person. Palm vein recognition has been raised as an efficient technique for detecting people without compromising data security because vein structures are under the skin and can only be captured in a living body. Besides, palm vein patterns are related to soft biometric traits such as gender and age by allowing the development of multi-task learning models. This paper introduces a multi-task extreme learning machine (ELM) model for palm vein multiclassification to process identity and soft biometrics data simultaneously. The proposed methodology evaluates a single ELM model that shares the hidden layer weights and has three outputs for identification, as well as gender and age classification of individuals. The evaluation was performed on the VERA palm vein database, which includes these soft biometric labeling metadata. Although no improvements were reached in terms of training time compared to a standard ELM, it is possible to simplify the number of neurons to optimize the multitask ELM. The proposed method contrasts with the standard ELM, which requires generating a specific model and adjusting the number of neurons for each task. Experimental results on multi-task classification show an average accuracy of 95% with 700 hidden neurons. These results indicate the feasibility of simultaneous identification and classification using a multi-task ELM on palm vein patterns.