Author
Ali Moeini
Bio: Ali Moeini is an academic researcher from Amirkabir University of Technology. The author has contributed to research in topics: Facial recognition system & Three-dimensional face recognition. The author has an hindex of 11, co-authored 22 publications receiving 279 citations.
Papers
More filters
TL;DR: 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 to demonstrate high accuracy and outperforms other approaches realistically.
Abstract: 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.
47 citations
TL;DR: 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.
Abstract: 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.
27 citations
TL;DR: This work introduces three real-world open-set face recognition methods across facial plastic surgery changes and a look-alike face by 3D face reconstruction and sparse representation, and proposes likeness dictionary learning.
Abstract: The open-set problem is among the problems that have significantly changed the performance of face recognition algorithms in real-world scenarios. Open-set operates under the supposition that not all the probes have a pair in the gallery. Most face recognition systems in real-world scenarios focus on handling pose, expression and illumination problems on face recognition. In addition to these challenges, when the number of subjects is increased for face recognition, these problems are intensified by look-alike faces for which there are two subjects with lower intra-class variations. In such challenges, the inter-class similarity is higher than the intra-class variation for these two subjects. In fact, these look-alike faces can be created as intrinsic, situation-based and also by facial plastic surgery. This work introduces three real-world open-set face recognition methods across facial plastic surgery changes and a look-alike face by 3D face reconstruction and sparse representation. Since some real-world databases for face recognition do not have multiple images per person in the gallery, with just one image per subject in the gallery, this paper proposes a novel idea to overcome this challenge by 3D modeling from gallery images and synthesizing them for generating several images. Accordingly, a 3D model is initially reconstructed from frontal face images in a real-world gallery. Then, each 3D reconstructed face in the gallery is synthesized to several possible views and a sparse dictionary is generated based on the synthesized face image for each person. Also, a likeness dictionary is defined and its optimization problem is solved by the proposed method. Finally, the face recognition is performed for open-set face recognition using three proposed representation classifications. Promising results are achieved for face recognition across plastic surgery and look-alike faces on three databases including the plastic surgery face, look-alike face and LFW databases compared to several state-of-the-art methods. Also, several real-world and open-set scenarios are performed to evaluate the proposed method on these databases in real-world scenarios. This paper uses 3D reconstructed models to recognize look-alike faces.A feature is extracted from both facial reconstructed depth and texture images.This paper proposes likeness dictionary learning.Three open-set classification methods are proposed for real-world face recognition.
27 citations
TL;DR: Favourable outcomes are acquired for gender classification on the labelled faces in the wild and FERET databases based on the proposed method compared to several state-of-the-arts in gender classification.
Abstract: In this study, a novel method is proposed for gender classification by adding facial depth features to texture features Accordingly, the three-dimensional (3D) generic elastic model is used to reconstruct the 3D model from human face using only a single 2D frontal image Then, the texture and depth are extracted from the reconstructed face model Afterwards, the local Gabor binary pattern (LGBP) is applied to both facial texture and reconstructed depth to extract the feature vectors from both texture and reconstructed depth images Finally, by combining 2D and 3D feature vectors, the final LGBP histogram bins are generated and classified by the support vector machine Favourable outcomes are acquired for gender classification on the labelled faces in the wild and FERET databases based on the proposed method compared to several state-of-the-arts in gender classification
20 citations
24 Aug 2014
TL;DR: Promising results were achieved for makeup-invariant face recognition on the available image database based on the present method compared to several state-of-the-art methods.
Abstract: In this paper, a novel feature extraction method is proposed to handle facial makeup in face recognition. To develop a face recognition method robust to facial makeup, features are extracted from face depth in which facial makeup is not effective. Then, face depth features are added to face texture features to perform feature extraction. Accordingly, a 3D face is reconstructed from only a single 2D frontal image with/without facial expressions. Then, the texture and depth of the face are extracted from the reconstructed model. Afterwards, the Dual-Tree Complex Wavelet Transform (DT-CWT) is applied to both texture and reconstructed depth of the face to extract the feature vectors from both texture and reconstructed depth images. Finally, by combining 2D and 3D feature vectors, the final feature vectors are generated and classified by the Support Vector Machine (SVM). Promising results were achieved for makeup-invariant face recognition on the available image database based on the present method compared to several state-of-the-art methods.
18 citations
Cited by
More filters
TL;DR: The inherent difficulties in PIFR are discussed and a comprehensive review of established techniques are presented, that is, pose-robust feature extraction approaches, multiview subspace learning approaches, face synthesis approaches, and hybrid approaches.
Abstract: The capacity to recognize faces under varied poses is a fundamental human ability that presents a unique challenge for computer vision systems. Compared to frontal face recognition, which has been intensively studied and has gradually matured in the past few decades, Pose-Invariant Face Recognition (PIFR) remains a largely unsolved problem. However, PIFR is crucial to realizing the full potential of face recognition for real-world applications, since face recognition is intrinsically a passive biometric technology for recognizing uncooperative subjects. In this article, we discuss the inherent difficulties in PIFR and present a comprehensive review of established techniques. Existing PIFR methods can be grouped into four categories, that is, pose-robust feature extraction approaches, multiview subspace learning approaches, face synthesis approaches, and hybrid approaches. The motivations, strategies, pros/cons, and performance of representative approaches are described and compared. Moreover, promising directions for future research are discussed.
269 citations
Posted Content•
TL;DR: A comprehensive review of pose-invariant face recognition methods can be found in this paper, where pose-robust feature extraction approaches, multi-view subspace learning approaches, face synthesis approaches, and hybrid approaches are compared.
Abstract: The capacity to recognize faces under varied poses is a fundamental human ability that presents a unique challenge for computer vision systems. Compared to frontal face recognition, which has been intensively studied and has gradually matured in the past few decades, pose-invariant face recognition (PIFR) remains a largely unsolved problem. However, PIFR is crucial to realizing the full potential of face recognition for real-world applications, since face recognition is intrinsically a passive biometric technology for recognizing uncooperative subjects. In this paper, we discuss the inherent difficulties in PIFR and present a comprehensive review of established techniques. Existing PIFR methods can be grouped into four categories, i.e., pose-robust feature extraction approaches, multi-view subspace learning approaches, face synthesis approaches, and hybrid approaches. The motivations, strategies, pros/cons, and performance of representative approaches are described and compared. Moreover, promising directions for future research are discussed.
263 citations
TL;DR: A novel model, named Deep Attentive Multi-path Convolutional Neural Network (DAM-CNN), that can automatically locate expression-related regions in an expressional image and yield a robust image representation for FER.
Abstract: Facial Expression Recognition (FER) has long been a challenging task in the field of computer vision. In this paper, we present a novel model, named Deep Attentive Multi-path Convolutional Neural Network (DAM-CNN), for FER. Different from most existing models, DAM-CNN can automatically locate expression-related regions in an expressional image and yield a robust image representation for FER. The proposed model contains two novel modules: an attention-based Salient Expressional Region Descriptor (SERD) and the Multi-Path Variation-Suppressing Network (MPVS-Net). SERD can adaptively estimate the importance of different image regions for FER task, while MPVS-Net disentangles expressional information from irrelevant variations. By jointly combining SERD and MPVS-Net, DAM-CNN is able to highlight expression-relevant features and generate a variation-robust representation for expression classification. Extensive experimental results on both constrained datasets (CK+, JAFFE, TFEID) and unconstrained datasets (SFEW, FER2013, BAUM-2i) demonstrate the effectiveness of our DAM-CNN model.
145 citations
TL;DR: An effective performance analysis of the proposed as well as the conventional methods such as convolutional neural network, NN-Levenberg-Marquardt, N nN-Gradient Descent, N N-Evolutionary Algorithm, Nn-firefly, and N n-Particle Swarm Optimisation is provided by evaluating few performance measures and thereby, the effectiveness of the suggested strategy over the conventional method is validated.
Abstract: The channels used to convey the human emotions consider actions, behaviours, poses, facial expressions, and speech. An immense research has been carried out to analyse the relationship between the facial emotions and these channels. The goal of this study is to develop a system for Facial Emotion Recognition (FER) that can analyse the elemental facial expressions of human, such as normal, smile, sad, surprise, anger, fear, and disgust. The recognition process of the proposed FER system is categorised into four processes, namely pre-processing, feature extraction, feature selection, and classification. After preprocessing, scale invariant feature transform -based feature extraction method is used to extract the features from the facial point. Further, a meta-heuristic algorithm called Grey Wolf optimisation (GWO) is used to select the optimal features. Subsequently, GWO-based neural network (NN) is used to classify the emotions from the selected features. Moreover, an effective performance analysis of the proposed as well as the conventional methods such as convolutional neural network, NN-Levenberg-Marquardt, NN-Gradient Descent, NN-Evolutionary Algorithm, NN-firefly, and NN-Particle Swarm Optimisation is provided by evaluating few performance measures and thereby, the effectiveness of the proposed strategy over the conventional methods is validated.
134 citations
TL;DR: A new model, called Local-DNN, is proposed for the gender recognition problem, based on local features and deep neural networks, which outperforms other deep learning methods also evaluated and obtains state-of-the-art results in both benchmarks.
Abstract: A new model, called Local-DNN, is proposed for the gender recognition problem.The model is based on local features and deep neural networks.The local contributions are combined in a voting scheme for the final classification.The model obtains state-of-the-art results in two wild face image datasets. Display Omitted Deep learning methods are able to automatically discover better representations of the data to improve the performance of the classifiers. However, in computer vision tasks, such as the gender recognition problem, sometimes it is difficult to directly learn from the entire image. In this work we propose a new model called Local Deep Neural Network (Local-DNN), which is based on two key concepts: local features and deep architectures. The model learns from small overlapping regions in the visual field using discriminative feed-forward networks with several layers. We evaluate our approach on two well-known gender benchmarks, showing that our Local-DNN outperforms other deep learning methods also evaluated and obtains state-of-the-art results in both benchmarks.
129 citations