Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation, and COTS Evaluation

Gonzalez-Sosa, Ester; Fiérrez, Julián; Vera-Rodríguez, Rubén; Alonso‐Fernandez, Fernando

doi:10.1109/tifs.2018.2807791

Cited by 111 publications

(70 citation statements)

References 37 publications

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“…The most commonly used databases for face recognition systems under different conditions are Pointing Head Pose Image Database (PHPID) [126], Labeled Faces in Wild (LFW) [127], FERET [15,16], ORL, and Yale. The last are used for face recognition systems under different conditions, which provide information for supervised and unsupervised learning.…”

Section: Databases Usedmentioning

confidence: 99%

Face Recognition Systems: A Survey

Kortli

Jridi²,

Falou³

et al. 2020

Sensors

387

146

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Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete applications that are gaining attraction among industries. Various techniques are being developed including local, holistic, and hybrid approaches, which provide a face image description using only a few face image features or the whole facial features. The main contribution of this survey is to review some well-known techniques for each approach and to give the taxonomy of their categories. In the paper, a detailed comparison between these techniques is exposed by listing the advantages and the disadvantages of their schemes in terms of robustness, accuracy, complexity, and discrimination. One interesting feature mentioned in the paper is about the database used for face recognition. An overview of the most commonly used databases, including those of supervised and unsupervised learning, is given. Numerical results of the most interesting techniques are given along with the context of experiments and challenges handled by these techniques. Finally, a solid discussion is given in the paper about future directions in terms of techniques to be used for face recognition.

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Section: Databases Usedmentioning

confidence: 99%

Face Recognition Systems: A Survey

Kortli

Jridi²,

Falou³

et al. 2020

Sensors

387

146

View full text Add to dashboard Cite

show abstract

“…In some cases, soft biometric attributes can be gleaned from low-resolution biometric data [95]. A review of techniques for extracting and using soft biometrics, especially in the case of face recognition, are provided in [96,97,98,94,99]. Table 3.2 presents examples of recent papers that utilize soft biometric information for recognition.…”

Section: Primary Biometrics and Soft Biometricsmentioning

confidence: 99%

A comprehensive overview of biometric fusion

2019

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The performance of a biometric system that relies on a single biometric modality (e.g., fingerprints only) is often stymied by various factors such as poor data quality or limited scalability. Multibiometric systems utilize the principle of fusion to combine information from multiple sources in order to improve recognition accuracy whilst addressing some of the limitations of singlebiometric systems. The past two decades have witnessed the development of a large number of biometric fusion schemes. This paper presents an overview of biometric fusion with specific focus on three questions: what to fuse, when to fuse, and how to fuse. A comprehensive review of techniques incorporating ancillary information in the biometric recognition pipeline is also presented. In this regard, the following topics are discussed: (i) incorporating data quality in the biometric recognition pipeline; (ii) combining soft biometric attributes with primary biometric identifiers; (iii) utilizing contextual information to improve biometric recognition accuracy; and (iv) performing continuous authentication using ancillary information. In addition, the use of information fusion principles for presentation attack detection and multibiometric cryptosystems is also discussed. Finally, some of the research challenges in biometric fusion are enumerated. The purpose of this article is to provide readers a comprehensive overview of the role of information fusion in biometrics.

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“…Advances in adaptive MCS can be instrumental for the future of this Challenge 1. In addition to such adaptive schemes, a better understanding of such unconstrained scenarios through benchmarks and public databases is also of outmost importance [65,66].…”

Section: Challenges In Biometrics: Role Of Mcsmentioning

confidence: 99%