A survey on heterogeneous face recognition: Sketch, infra-red, 3D and low-resolution

Ouyang, Shuxin; Hospedales, Timothy M.; Song, Yi-Zhe; Li, Xueming; Loy, Chen Change; Wang, Xiaogang

doi:10.1016/j.imavis.2016.09.001

Cited by 109 publications

(57 citation statements)

References 102 publications

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“…Several surveys have been published. To name a few, good surveys exist for illumination-invariant face recognition Zou et al (2007), 3D face recognition Bowyer et al (2006), single image-based face recognition Tan et al (2006), video-based face recognition Barr et al (2012), and heterogeneous face recognition Ouyang et al (2014). There are also comprehensive surveys on various aspects of face recognition Zhao et al (2003).…”

Section: Related Workmentioning

confidence: 99%

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A Comprehensive Survey on Pose-Invariant Face Recognition

Ding

Tao

2016

ACM Trans. Intell. Syst. Technol.

261

137

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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.

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Section: Related Workmentioning

confidence: 99%

“…Inspired by Ouyang et al (2014), we unify the four categories of PIFR approaches in the following formulation.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey on Pose-Invariant Face Recognition

Ding

Tao

2016

ACM Trans. Intell. Syst. Technol.

261

137

View full text Add to dashboard Cite

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“…Methods that convert a photo to a sketch can be classified into two types: supervised [7,8] and unsupervised methods [3,12]. The supervised methods convert a photo to sketch by learning sketch features from a training set.…”

Section: Related Workmentioning

confidence: 99%

An unsupervised method for face photo-sketch synthesis and recognition

Abdel-Aziz

Ebeid

Mostafa

2016

2016 7th International Conference on Information and Communication Systems (ICICS)

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Face recognition is considered one of the most essential applications of Biometrics for personal identification. Face sketch recognition is a special case of face recognition, and it is very important for forensic applications. In this paper, we propose an unsupervised method for face photo-sketch recognition by synthesizing a pseudo-sketch from a single photo. The proposed method is the first unsupervised method that deals with face sketch recognition. The proposed photo-sketch synthesis step consists of two main steps, namely: edge detection and hair detection, which are applied on the grayscale image of the photo image. In the recognition step, the artist sketch is compared with the generated pseudo-sketch. PCA and LDA are used to extract features from the sketch images. The k-nearest neighbor classifier with Euclidean distance is used in the classification step. We use the CUHK database to test the performance of the proposed Method. Results for the synthesized sketches are compared with state-of-the-art methods, e.g., Local Linear Embedding (LLE) and Eigen transformation. The experimental results show that the proposed method generates a clear synthesis sketch and it defines persons more accurate than other methods. Moreover, in the recognition step, the proposed method achieves a recognition rate at the 1-nearest neighbor (rank1: first-match) range from 82% with PCA to 94% with LDA. The highest recognition rate is obtained at the 5-nearest neighbor (rank 5) is 98% that is better than some of the state-of-the-art methods.

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“…In recent years, there has been significant amount of research in Heterogeneous Face Recognition (HFR) [17]. The main issue in HFR is to match the visible face image to a face image that has been captured in another domain such as in the infrared spectrum [17], or the polarimetric thermal [7] domain. Infrared images are categorized into two major groups of reflection and emission.…”

Section: Introductionmentioning

confidence: 99%

“…The emission category contains the midwave infrared (MWIR) and longwave infrared (LWIR) bands and it is less informative [17] compared with the reflection category. Due to the significant phenomenological differences between the distribution of thermal and visible imagery, matching a thermal face against a gallery of visible faces becomes a challenging task.…”

Section: Introductionmentioning

confidence: 99%

Attribute-Guided Deep Polarimetric Thermal-to-visible Face Recognition

Iranmanesh

Nasrabadi

2019

2019 International Conference on Biometrics (ICB)

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In this paper, we present an attribute-guided deep coupled learning framework to address the problem of matching polarimetric thermal face photos against a gallery of visible faces. The coupled framework contains two subnetworks, one dedicated to the visible spectrum and the second sub-network dedicated to the polarimetric thermal spectrum. Each sub-network is made of a generative adversarial network (GAN) architecture. We propose a novel Attribute-Guided Coupled Generative Adversarial Network (AGC-GAN) architecture which utilizes facial attributes to improve the thermal-to-visible face recognition performance. The proposed AGC-GAN exploits the facial attributes and leverages multiple loss functions in order to learn rich discriminative features in a common embedding subspace. To achieve a realistic photo reconstruction while preserving the discriminative information, we also add a perceptual loss term to the coupling loss function. An ablation study is performed to show the effectiveness of different loss functions for optimizing the proposed method. Moreover, the superiority of the model compared to the state-ofthe-art models is demonstrated using polarimetric dataset.

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A survey on heterogeneous face recognition: Sketch, infra-red, 3D and low-resolution

Cited by 109 publications

References 102 publications

A Comprehensive Survey on Pose-Invariant Face Recognition

A Comprehensive Survey on Pose-Invariant Face Recognition

An unsupervised method for face photo-sketch synthesis and recognition

Attribute-Guided Deep Polarimetric Thermal-to-visible Face Recognition

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