Approach For Head Pose Estimation Research Articles

Multi-Task Head Pose Estimation in-the-Wild.

We present a deep learning-based multi-task approach for head pose estimation in images. We contribute with a network architecture and training strategy that harness the strong dependencies among face pose, alignment and visibility, to produce a top performing model for all three tasks. Our architecture is an encoder-decoder CNN with residual blocks and lateral skip connections. We show that the combination of head pose estimation and landmark-based face alignment significantly improve the performance of the former task. Further, the location of the pose task at the bottleneck layer, at the end of the encoder, and that of tasks depending on spatial information, such as visibility and alignment, in the final decoder layer, also contribute to increase the final performance. In the experiments conducted the proposed model outperforms the state-of-the-art in the face pose and visibility tasks. By including a final landmark regression step it also produces face alignment results on par with the state-of-the-art.

Real-World and Rapid Face Recognition Toward Pose and Expression Variations via Feature Library Matrix

In this paper, a novel method for face recognition under pose and expression variations is proposed from only a single image in the gallery. A 3D probabilistic facial expression recognition generic elastic model is proposed to reconstruct a 3D model from real-world human face using only a single 2D frontal image with/without facial expressions. Then, a feature library matrix (FLM) is generated for each subject in the gallery from all face poses by rotating the 3D reconstructed models and extracting features in the rotated face pose. Therefore, each FLM is subsequently rendered for each subject in the gallery based on triplet angles of face poses. In addition, before matching the FLM, an initial estimate of triplet angles is obtained from the face pose in probe images using an automatic head pose estimation approach. Then, an array of the FLM is selected for each subject based on the estimated triplet angles. Finally, the selected arrays from FLMs are compared with extracted features from the probe image by iterative scoring classification using the support vector machine. Convincing results are acquired to handle pose and expression changes on the Bosphorus, Face Recognition Technology (FERET), Carnegie Mellon University-Pose, Illumination, and Expression (CMU-PIE), and Labeled Faces in the Wild (LFW) face databases compared with several state-of-the-art methods in pose-invariant face recognition. The proposed method not only demonstrates an excellent performance by obtaining high accuracy on all four databases but also outperforms other approaches realistically.

Unrestricted pose-invariant face recognition by sparse dictionary matrix

In this paper, a novel method is proposed for real-world pose-invariant face recognition from only a single image in a gallery. A 3D Facial Expression Generic Elastic Model (3D FE-GEM) is proposed to reconstruct a 3D model of each human face using only a single 2D frontal image. Then, for each person in the database, a Sparse Dictionary Matrix (SDM) is created from all face poses by rotating the 3D reconstructed models and extracting features in the rotated face. Each SDM is subsequently rendered based on triplet angles of face poses. Before matching to SDM, an initial estimate of triplet angles of face poses is obtained in the probe face image using an automatic head pose estimation approach. Then, an array of the SDM is selected based on the estimated triplet angles for each subject. Finally, the selected arrays from SDMs are compared with the probe image by sparse representation classification. Convincing results were acquired to handle pose changes on the FERET, CMU PIE, LFW and video face databases based on the proposed method compared to several state-of-the-art in pose-invariant face recognition.

Illumination invariant head pose estimation using random forests classifier and binary pattern run length matrix

AbstractIn this paper, a novel approach for head pose estimation in gray-level images is presented. In the proposed algorithm, two techniques were employed. In order to deal with the large set of training data, the method of Random Forests was employed; this is a state-of-the-art classification algorithm in the field of computer vision. In order to make this system robust in terms of illumination, a Binary Pattern Run Length matrix was employed; this matrix is combination of Binary Pattern and a Run Length matrix. The binary pattern was calculated by randomly selected operator. In order to extract feature of training patch, we calculate statistical texture features from the Binary Pattern Run Length matrix. Moreover we perform some techniques to real-time operation, such as control the number of binary test. Experimental results show that our algorithm is efficient and robust against illumination change.

An effective head pose estimation approach using Lie Algebrized Gaussians based face representation

The accuracy of head pose estimation is significant for many computer vision applications such as face recognition, driver attention detection and human-computer interaction. Most appearance-based head pose estimation works typically extract the low-dimensional face appearance features in some statistic subspaces, where the subspaces represent the underlying geometry structure of the pose space. However, there is an open problem, namely, how to effectively represent appearance-based subspace face for the head pose estimation problem. To address the problem, this paper proposes a head pose estimation approach based on the Lie Algebrized Gaussians (LAG) feature to model the pose characteristic. LAG is built on Gaussian Mixture Models (GMM), which actually not only models the distribution of local appearance features, but also captures the Lie group manifold structure of the feature space. Moreover, to keep multi-resolution structure information, LAG is operated on many subregions of the image. As a result, these properties of LAG enable it to effectively model the structure of subspace face which can lead to powerful discriminative ability for head pose estimation. After representing subspace face using the LAG, we treat the head pose estimation as a classification problem. The within-class covariance normalization (WCCN) based Support Vector Machine (SVM) classifier is employed to achieve robust performance as WCCN could reduce the within-class variabilities of the same pose. Extensive experimental analysis and comparison with both traditional and state-of-the-art algorithms on two challenging benchmarks demonstrate the effectiveness of our approach.