WT-BiFPN: A Novel Approach for Efficient Face Detection using Improved EfficientDet Architecture and Dual-Tree Complex Wavelet Transform

Abstract

Face detection is a crucial task in computer vision and image processing with various practical applications including security, surveillance and entertainment. In recent years, approaches based on deep learning methods improved significantly with high-accuracy detection results. In this paper, we propose a new framework for anefficient face detection based on improved EfficientDet architecture and wavelet transform. Our method utilizes a combination of innovated bi-directional feature pyramid network (BiFPN) and a dual-tree complex wavelet transform called WT-BiFPN to ameliorate feature representation of faces at multiple scales. We evaluate our approach on two benchmark datasets, including WIDER FACE and our gathered dataset with specific details close to real-world images. Our proposed architecture achieves more than 5% performance improvement on previous state-of-the-art EfficientDet-based methods in terms of mean average precision (mAP). Our method provides an effective accurate face detection solution for several applications and can prevail over low resolution and occlusion in images.