Pose-Invariant Face Recognition via Facial Landmark Based Ensemble Learning

In recent years, pose-invariant face recognition has been mainly approached from a holistic insight. DCNNs (ArcFace, Elastic Face, FaceNet) are used to compute a face image embedding, which is used later to perform face recognition. This paper presents a novel approach to pose-invariant face recognition through the use of ensemble learning and local feature descriptors. The proposed method trains a base learner for each person's face recognition ensemble system, based on feature vectors (SIFT, GMM, LBP) extracted from image regions surrounding specific facial landmarks. Three different classification models (SVM, Naive Bayes, GMM) are exclusively used as base learners, and the training procedure for each of these models is detailed. The proposed methodology includes a novel face pose descriptor referred to as the Face Angle Vector (FAV) which is utilized by a head pose classification model to determine the pose class of a face image. This model works together with a Base Learner Selection (BLS) block, to determine a set of facial landmarks to extract local feature descriptors, and uses them as the input to their corresponding base learners. Experimental results show a better performance over state-of-the-art methods using the CMU-PIE as the testing dataset, and face poses within +/- 90 degrees.

Automated summary

This paper presents a novel approach to achieve pose-invariant face recognition using ensemble learning and local feature descriptors. The proposed method extracts feature vectors from image regions surrounding specific facial landmarks, and trains three different classification models as base learners. Experimental results show better performance compared to existing methods using the CMU-PIE dataset.