Facial Attribute Recognition Using Lightweight Multi-Label CNN-Transformer Architecture for Intelligent Advertising

In modern cities, intelligent advertising platforms have been widely engaged in public areas. A facial attribute recognition technique is essential to assist these platforms in delivering suitable adverts for each audience. These platforms also require a recognition technology that can operate at least suitably on a CPU device to reduce implementation costs. This work proposed a lightweight multi-label CNN-Transformer architecture with an efficient inception block (EIB) and squeeze channel transformer encoder (SCTE) to perform facial attribute recognition efficiently. EIB is used to extract face features in multi-scale and levels supported by SCTE in improving its feature map's quality. The proposed architecture produces fewer parameters with low operations and gains competitive accuracy on the CelebA and LWFA datasets consisting of images with multi-label. Moreover, the proposed architecture integrated with face detection can perform sufficiently on a CPU configuration in real-time with 21 frames per second (FPS) using 224 × 224 input size of face area image.