EMPCNet: Facial Attribute Recognition Using Efficient Multi - Perspective Convolution for Human-Robot Interaction

Human-robot interaction has evolved into a significant field in robotics. In this domain, facial attributes are essential as they enable robots to understand human emotions, intentions, and preferences. In robot applications, which typically involve low-cost devices, efficient recognition technology is crucial for promising real-time operation by robots. This work proposes EMPCNet to perform facial attribute recognition, consisting of an Efficient Multi-Perspective Convolution (EMPC) block used to efficiently extract and capture various information from multiple perspectives using different kernel sizes and shapes of convolutional operations. The proposed network, which only utilizes a few parameters and low computational operations, achieves competitive performance on the CelebA and LFWA datasets. Additionally, when integrated with face detection, the proposed EMPCNet operates efficiently in real-time on a CPU with Intel Core i7-9750H, achieving a frame rate of 21.27 frames per second (FPS) with an image input size of
$224\times 224$
consisting of a face area.