Abstract
This study proposes a lightweight multilayer random forest (LMRF) model, which is a non-neural network style deep model consisting of layer-by-layer random forests. Although a deep neural network (DNN) is a powerful algorithm for facial expression recognition (FER), the requirement of too many parameters, careful parameter tuning, a huge amount of training data, black-box models, and a pre-trained architecture remain significant burdens for a current DNN, particularly for real-time processing. To overcome the limitations of a DNN, our FER system uses LMRF consisting of a two-layer structure and a small number of trees per layer for fast FER. The proposed LMRF achieves a similar performance as a DNN even with a small number of hyper-parameters, and a faster processing time using a CPU. We conducted experiments using a benchmark database captured indoors and a real driving database captured using a near-infrared (NIR) camera. Based on a performance evaluation against a few other state-of-the-art FER methods, the proposed method showed a more uniform performance than DNN-based methods, and required a reduced number of parameters and operations without a loss of accuracy when compared to DNN model compression algorithms. As a replacement for deeper and wider networks, the proposed model can be embedded in low-power and low-memory in vehicle systems for the monitoring of a driver's emotion.
Highlights
With the recent increase in interest regarding autonomous vehicles, advanced driver assistant systems (ADASs) have been researched as a core technology of autonomous vehicles
driver status monitoring (DSM)-related researches can be divided into three categories: 1) driving patterns based on moving records, 2) psycho-physiological states based on sensor information, and 3) in-vehicle image analysis based on a camera sensor
We introduced the short version of an lightweight multilayer random forest (LMRF) model [26] for facial expression recognition (FER) system
Summary
With the recent increase in interest regarding autonomous vehicles, advanced driver assistant systems (ADASs) have been researched as a core technology of autonomous vehicles. The detail contributions of this paper are as follows; 1) we propose an LMRF that has a deep structure based on layerby-layer processing using ensemble RFs instead of a neural network, which requires backpropagation, 2) LMRF offers fewer hyper-parameters and a faster processing time than a DNN while maintaining a similar performance, 3) apart from a CNN, this study try to improve the performance by applying face landmark detection and facial area cropping as a preprocessing, 4) we conduct experiments using public benchmark FER datasets, and a new dataset captured using a near-infrared (NIR) in a real driving environment, 5) through enough experiments, we prove that the proposed approach can be embedded into low-power and low-memory systems in place of a deep and wide network. The extracted geometric features are applied to the proposed LMRF with a N-layer structure, and we classify final facial expression with the maximum average value of the output probabilities of each RF of the last LMRF layer
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