A COMPARISON STUDY OF FACIAL FEATURE EXTRACTION USING MTCNN, RETINAFACE AND DLIB FACE DETECTOR FOR PERSONALITY TRAITS RECOGNITION

Abstract

Facial feature extraction is a fundamental step in various computer vision tasks including face recognition, emotion detection and personality traits recognition. The efficiency of these tasks depends on choosing the right face detector model to extract facial features. As for personality traits recognition tasks, face detection is important in understanding facial expressions that underlying personality traits. There are several face detector models like Multi-Task Cascaded Convolutional Neural Network (MTCNN), RetinaFace, and DLIB that can detect and extract facial features. However, the challenge arises in selecting the most effective face detector model, particularly when dealing with diverse facial expressions, orientations and occlusion. There is a lack of comprehensive comparisons that have been made between MTCNN, RetinaFace, and DLIB for face detection ability, particularly in video-based personality traits recognition. Thus, this study presents a comparative analysis of MTCNN, RetinaFace, and DLIB models, focusing on their ability to detect human face from key frames that are extracted from videos. This study used the ChaLearn dataset, which consists of 15-second videos of people speaking in front of a camera. MTCNN and RetinaFace were able to detect higher numbers of faces consistently, even in cases where the faces were not strictly frontal. In contrast, DLIB has problems detecting non-frontal faces and resulted in fewer faces detection. We demonstrate that MTCNN and RetinaFace are more suitable for tasks that require robust faces detection, especially across datasets that consist of a variety of facial poses. Additionally, using MTCNN and RetinaFace as face detector models gives prominent accuracy performance for video-based personality recognition.