Image recognition based on discriminative models using features generated from separable lattice HMMS

Abstract

This paper presents an image recognition technique based on discriminative models using features generated from separable lattice hidden Markov models (SL-HMMs). A major problem in image recognition is that the recognition performance is degraded by geometric variations such as that in position and size of the object to be recognized. SL-HMMs have been proposed to solve this problem. SL-HMMs are an extension of HMMs with size and locational invariances based on state transitions. An SL-HMM is a generative model and can represent generation processes of observations well. However, there is a possibility that the recognition performance of generative models is inferior to that of discriminative models because discriminative models are specialized to identification. In this paper, we propose image recognition based on log linear models (LLMs) using features extracted from SL-HMMs. The proposed method can extract features invariant to geometric variations by using SL-HMMs and built an accurate classifier based on discriminative models with the extracted features. Face recognition experiments showed that the proposed method obtained higher recognition rates than SL-HMMs and convolutional neural networks based methods.