Evaluation Of Hidden Markov Models Using Deep CNN Features In Isolated Sign Recognition

TL;DR

This paper empirically evaluates Hidden Markov Models combined with deep CNN features for isolated sign language recognition, demonstrating competitive accuracy with faster training and inference compared to LSTM models.

Contribution

It introduces a framework integrating deep CNN features with HMMs for sign recognition and proposes CNN-based dimension reduction architectures, filling a gap in empirical analysis of HMMs with deep features.

Findings

HMMs achieved 90.15% accuracy on Montalbano dataset.

Deep CNN features can be effectively used with HMMs for sign recognition.

HMMs are faster and require less computational resources than LSTM models.

Abstract

Isolated sign recognition from video streams is a challenging problem due to the multi-modal nature of the signs, where both local and global hand features and face gestures needs to be attended simultaneously. This problem has recently been studied widely using deep Convolutional Neural Network (CNN) based features and Long Short-Term Memory (LSTM) based deep sequence models. However, the current literature is lack of providing empirical analysis using Hidden Markov Models (HMMs) with deep features. In this study, we provide a framework that is composed of three modules to solve isolated sign recognition problem using different sequence models. The dimensions of deep features are usually too large to work with HMM models. To solve this problem, we propose two alternative CNN based architectures as the second module in our framework, to reduce deep feature dimensions effectively. After extensive experiments, we show that using pretrained Resnet50 features and one of our CNN based dimension reduction models, HMMs can classify isolated signs with 90.15% accuracy in Montalbano dataset using RGB and Skeletal data. This performance is comparable with the current LSTM based models. HMMs have fewer parameters and can be trained and run on commodity computers fast, without requiring GPUs. Therefore, our analysis with deep features show that HMMs could also be utilized as well as deep sequence models in challenging isolated sign recognition problem.