Cosmetic Detection Framework for Face and Iris Biometrics

Sharifi, Omid; Eskandari, Maryam

doi:10.3390/sym10040122

Cited by 11 publications

(5 citation statements)

References 32 publications

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“…They found its impact with the two dissimilar face matchers, and further spoofed faces were achieved, which matched better after the use of makeup. Omid and Maryam (2018) works aimed to demonstrate a recognition of cosmetic-applied face and iris biometrics by the fusion of texture, shape and color form of the images.…”

Section: Literature Reviewmentioning

confidence: 99%

Cosmetic applied based face recognition for biometric passport

Choudhury

Rabbani

2019

IJIUS

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Purpose Nowadays, the use of forged e-passport is increasing, which is threatening national security. It is important to improve the national security against international crime or terrorism. There is a weak verification process caused by lack of identification processes such as a physical check, biometric check and electronic check. The e-passport can prevent the passport cloning or forging resulting from the illegal immigration. The paper aims to discuss these issues. Design/methodology/approach This paper focuses on face recognition to improve the biometric authentication for an e-passport, and it also introduces facial permanent mark detection from the makeup or cosmetic-applied faces, twins and similar faces. An algorithm is proposed to detect the cosmetic-applied facial permanent marks such as mole, freckle, birthmark and pockmark. Active Shape Model into Active Appearance Model using Principal Component Analysis is applied to detect the facial landmarks. Facial permanent marks are detected by applying the Canny edge detector and Gradient Field Histogram of Oriented Gradient. Findings This paper demonstrated an algorithm and proposed facial marks detection from cosmetic or makeup-applied faces for a secure biometric passport in the field of personal identification for national security. It also presented to detect and identify identical twins and similar faces. This paper presented facial marks detection from the cosmetic-applied face, which can be mixed with traditional methods. However, the use of the proposed technique faced some challenges due to the use of cosmetic. The combinations of the algorithm for facial mark recognition matching with classical methods were able to attain lower errors in this proposed experiment. Originality/value The proposed method will enhance the national security and it will improve the biometric authentication for the e-passport. The proposed algorithm is capable of identifying facial marks from cosmetic-applied faces accurately, with less false positives. The proposed technique shows the best results.

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Section: Literature Reviewmentioning

confidence: 99%

Cosmetic applied based face recognition for biometric passport

Choudhury

Rabbani

2019

IJIUS

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“…For example, distinct types of plastic surgery have been shown to also negatively affect periocular recognition [89] and ear recognition [90]. Similarly, the presence of makeup has been shown to hamper reliable sex prediction from iris images [91], while cosmetic lenses can be used to perform presentation attacks on iris recognition systems [92], [93]. Other types of beautification Apart from plastic surgery, makeup and facial retouching, there are further types of facial beautification that might impact face recognition systems.…”

Section: Issues and Challengesmentioning

confidence: 99%

Impact and Detection of Facial Beautification in Face Recognition: An Overview

2019

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Facial beautification induced by plastic surgery, cosmetics or retouching has the ability to substantially alter the appearance of face images. Such types of beautification can negatively affect the accuracy of face recognition systems. In this work, a conceptual categorisation of beautification is presented, relevant scenarios with respect to face recognition are discussed, and related publications are revisited. Additionally, technical considerations and trade-offs of the surveyed methods are summarized along with open issues and challenges in the field. This survey is targeted to provide a comprehensive point of reference for biometric researchers and practitioners working in the field of face recognition, who aim at tackling challenges caused by facial beautification.

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“…Hand-crafted feature based approaches extract local or global features from query and gallery faces to perform matching. The representative works in this category include [4,7,8,[12][13][14]. In [4], authors extracted LBP and Gabor features to capture micropatterns and shape and texture information, respectively.…”

Section: Hand-crafted Feature Based Approachesmentioning

confidence: 99%

“…In [12], Gabor wavelets and LBP descriptors are used as shape and texture descriptors respectively, for automatic facial makeup detection with application in face recognition. Similarly, a cosmetic detection approach is presented in [13] by leveraging a multiscale local-global technique. Guo et al [14] proposed a makeup-invariant face verification strategy based on the feature correlation between original and makeup wearing faces.…”

Section: Hand-crafted Feature Based Approachesmentioning

confidence: 99%

Data Augmentation-Assisted Makeup-Invariant Face Recognition

Sajid

Ali

Dar

et al. 2018

Mathematical Problems in Engineering

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Recently, face datasets containing celebrities photos with facial makeup are growing at exponential rates, making their recognition very challenging. Existing face recognition methods rely on feature extraction and reference reranking to improve the performance. However face images with facial makeup carry inherent ambiguity due to artificial colors, shading, contouring, and varying skin tones, making recognition task more difficult. The problem becomes more confound as the makeup alters the bilateral size and symmetry of the certain face components such as eyes and lips affecting the distinctiveness of faces. The ambiguity becomes even worse when different days bring different facial makeup for celebrities owing to the context of interpersonal situations and current societal makeup trends. To cope with these artificial effects, we propose to use a deep convolutional neural network (dCNN) using augmented face dataset to extract discriminative features from face images containing synthetic makeup variations. The augmented dataset containing original face images and those with synthetic make up variations allows dCNN to learn face features in a variety of facial makeup. We also evaluate the role of partial and full makeup in face images to improve the recognition performance. The experimental results on two challenging face datasets show that the proposed approach can compete with the state of the art.

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Cosmetic Detection Framework for Face and Iris Biometrics

Cited by 11 publications

References 32 publications

Cosmetic applied based face recognition for biometric passport

Cosmetic applied based face recognition for biometric passport

Impact and Detection of Facial Beautification in Face Recognition: An Overview

Data Augmentation-Assisted Makeup-Invariant Face Recognition

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