Spoof detection on face and palmprint biometrics

Farmanbar, Mina; Toygar, Önsen

doi:10.1007/s11760-017-1082-y

Cited by 35 publications

(24 citation statements)

References 20 publications

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“…The HOG descriptor is one of the most popular approaches for object detection. It is invariant to illumination and geometric transformations and it has been successfully applied to many security applications, such as privacy in image feature extraction using homomorphic encryption [40], phishing detection [41], classification of sensitive information embedded within uploaded photos [42], handwritten digits recognition [43], facial expression recognition with CNNs [44] and, particularly, to face spoofing detection [11,[45][46][47][48]. Due to its popularity in anti-spoofing detection, in this work a variant of the HOG descriptor will be presented and experimentally validated.…”

Section: Related Workmentioning

confidence: 99%

“…Because of this, most security systems provide some kind of protection such as hashing, digital signature or encryption that are ineffective in spoof attacks [5]. In the last few years there has been an intensive research to provide reliable anti-spoofing systems for biometric traits, including fingerprints [6,7], face [8][9][10], and other biometric features [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Entropy-Based Face Recognition and Spoof Detection for Security Applications

2019

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Nowadays, cyber attacks are becoming an extremely serious issue, which is particularly important to prevent in a smart city context. Among cyber attacks, spoofing is an action that is increasingly common in many areas, such as emails, geolocation services or social networks. Identity spoofing is defined as the action by which a person impersonates a third party to carry out a series of illegal activities such as committing fraud, cyberbullying, sextorsion, etc. In this work, a face recognition system is proposed, with an application to the spoofing prevention. The method is based on the Histogram of Oriented Gradients (HOG) descriptor. Since different face regions do not have the same information for the recognition process, introducing entropy would quantify the importance of each face region in the descriptor. Therefore, entropy is added to increase the robustness of the algorithm. Regarding face recognition, our approach has been tested on three well-known databases (ORL, FERET and LFW) and the experiments show that adding entropy information improves the recognition rate significantly, with an increase over 40% in some of the considered databases. Spoofing tests has been implemented on CASIA FASD and MIFS databases, having obtained again better results than similar texture descriptors approaches.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Entropy-Based Face Recognition and Spoof Detection for Security Applications

2019

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“…Finally, multimodal biometric systems are less likely to be spoofed as impostors, since one has to forge multiple biometric features at the same time. For this reason, different works addressed the impostor problem by combining two or more human characteristics [91,92,93,94,95,96,97].…”

Section: Future Perspectivesmentioning

confidence: 99%

How far did we get in face spoofing detection?

Souza

Oliveira

Pamplona

et al. 2018

Engineering Applications of Artificial Intelligence

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The growing use of control access systems based on face recognition shed light over the need for even more accurate systems to detect face spoofing attacks. In this paper, an extensive analysis on face spoofing detection works published in the last decade is presented. The analyzed works are categorized by their fundamental parts, i.e., descriptors and classifiers. This structured survey also brings a comparative performance analysis of the works considering the most important public data sets in the field. The methodology followed in this work is particularly relevant to observe temporal evolution of the field, trends in the existing approaches, * Corresponding author: Luciano Oliveira, tel. +55 71 3283-9472Email addresses: luiz.otavio@ufba.br (Luiz Souza), lrebouca@ufba.br (Luciano Oliveira), mauricio@dcc.ufba.br (Mauricio Pamplona), papa@fc.unesp.br (Joao Papa) to discuss still opened issues, and to propose new perspectives for the future of face spoofing detection.

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“…A method for detecting presentation attacks in dorsal hand-vein biometrics is introduced by Bhilare et al in [14], employing a histogram of oriented gradients performed on LoG-filtered images and an SVM with majority voting for image classification, reaching EER from 0.16% to 0.8%. A fusion of texture-based approaches and image quality assessment for face and palmprint PAD is introduced by Farmanbar and Toygar in [15], and tested on several publicly available datasets of face and palmprint samples. Bhilare et al followed up on their earlier work in [16], introduing a spoof sample database -'PALMspoof', and a PAD method that is said to outperform their earlier LBP-based approach by 12.73 percentage points in classification error rate.…”

Section: Related Workmentioning

confidence: 99%

Thermal Features for Presentation Attack Detection in Hand Biometrics

Bartuzi

Trokielewicz

2018

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)

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This paper proposes a method for utilizing thermal features of the hand for the purpose of presentation attack detection (PAD) that can be employed in a hand biometrics system's pipeline. By envisaging two different operational modes of our system, and by employing a DCNNbased classifiers fine-tuned with a dataset of real and fake hand representations captured in both visible and thermal spectrum, we were able to bring two important deliverables. First, a PAD method operating in an open-set mode, capable of correctly discerning 100% of fake thermal samples, achieving Attack Presentation Classification Error Rate (APCER) and Bona-Fide Presentation Classification Error Rate (BPCER) equal to 0%, which can be easily implemented into any existing system as a separate component. Second, a hand biometrics system operating in a closed-set mode, that has PAD built right into the recognition pipeline, and operating simultaneously with the userwise classification, achieving rank-1 recognition accuracy of up to 99.75%. We also show that thermal images of the human hand, in addition to liveness features they carry, can also improve classification accuracy of a biometric system, when coupled with visible light images. To follow the reproducibility guidelines and to stimulate further research in this area, we share the trained model weights, source codes, and a newly created dataset of fake hand representations with interested researchers.

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Spoof detection on face and palmprint biometrics

Cited by 35 publications

References 20 publications

Entropy-Based Face Recognition and Spoof Detection for Security Applications

Entropy-Based Face Recognition and Spoof Detection for Security Applications

How far did we get in face spoofing detection?

Thermal Features for Presentation Attack Detection in Hand Biometrics

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