Face spoof detection using light reflection in moderate to low lighting

Mhou, Kudzaishe; Haar, Dustin van der; Leung, Wai Sze

doi:10.1109/acirs.2017.7986063

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…There is a need to explore alternative feature extraction techniques, develop comprehensive countermeasures against various types of attacks, evaluate the method on diverse spoofing databases, and conduct comparative analyses with existing techniques. K. Mhou et al Mhou et al (2017) presented a method for detecting face spoof attacks using vision infrared and reflection patterns. The authors employed various techniques, such as Laplacian blur detection, Gabor filters, color moments, and LBP, to authenticate and measure the authenticity of a face.…”

Section: Literature Surveymentioning

confidence: 99%

Exponentiating the Precision of Facial Spoof Detection: An Intricately Woven Tapestry of Hybrid Machine Learning Paradigms and State-of-the-Art Textural Feature Extraction Methodologies

Singh,

Anand,

Sharma

2023

Preprint

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In recent times, there has been a rapid increase in the deployment of face recognition systems,creating a significant demand for stringent safety regulations to govern their implementation. Several approaches have been developed for face spoof detection, utilizing techniques such as feature extraction, machine learning (ML) and deep learning (DL). These methods have gained significant interest in various domains due to their accessibility and ability to capture abundant information in daily life. Face spoof detection techniques based on ML and Artificial Intelligence (AI) typically involve multiple stages, including pre-processing the raw data, feature extraction, and face classification. Previous methods have employed K-Nearest Neighbor (KNN) in combination with Grey Level Co-occurrence Matrix (GLCM) for detecting face spoofing, but with relatively low accuracy.In this study, a novel approach is introduced that combines Histogram of Oriented Gradients (HOG) with GLCM for feature extraction. These features are then utilized in a hybrid classifier, which integrates the Random Forest (RF) algorithm boosted with Adaboost optimizer, K-Nearest Neighbor (KNN), and XGBoost as classification algorithms. Through extensive evaluation of our proposed strategies and thorough comparison, our approach demonstrates its superiority over alternative methods, achieving remarkable levels of accuracy, precision, and recall performance.

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Section: Literature Surveymentioning

confidence: 99%

Exponentiating the Precision of Facial Spoof Detection: An Intricately Woven Tapestry of Hybrid Machine Learning Paradigms and State-of-the-Art Textural Feature Extraction Methodologies

Singh,

Anand,

Sharma

2023

Preprint

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“…In particular, the light source while the sample was photographing the previous processing provided excellent results. Investigators also extracted a 40-person data set with different cameras that could serve as other related sources CASIA-FASD and the MSU MFSD public datasets [9]. In .…”

Section: Related Workmentioning

confidence: 99%

A Face Spoofing Detection System Using Surf Analysis Based on Genetic Algorithm and Artificial Intelligence Technique.

2020

IJESRT

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Modern face biometric systems are susceptible to spoofing attacks and a secure face spoof detection system demands the capability to recognize whether a face is from a real person or a spoofed image that is created by an unauthenticated person. Inspired by the feature selection algorithm, characterization of printing artifacts, and differences in light reflection, we proposed to approach the problem of spoofing detection from a pattern analysis point of view. Indeed, face prints often contain printing quality faults that can be well detected using pattern features, the Speech up Robust Feature (SURF) descriptor. Hence, introduces a novel approach based on face pattern image analysis to find out if there is live in front of a camera or a printed face. The proposed approach analyzes the pattern and quality of the facial images using the SURF descriptor as a feature extraction algorithm. Compared to a lot of previous works, our proposed face spoofing detection approach is robust, computationally fast, and does not require user-cooperation. In addition, the feature optimization technique is used for the selection of a unique feature set from the ROI of face images. Convolutional Neural Network (CNN) classifier is used for the training of the proposed spoof detection system. It is seen that the designed hybrid system face spoof detection achieves high performance than the existing system and execution time is also well. The proposed method is assessed using the MATLAB simulator in computer vision and image processing toolbox. The experimental analysis on a publicly accessible database presented brilliant results compared to existing works by using the concept of feature optimization and artificial intelligence technique.

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“…Mhou et al [12] proposed a method that also utilises the light reflection patterns for fake biometrics detection. The implemented system uses Laplacian blur detection, Gabor filters, colour moments and local binary patterns as features.…”

Section: Related Workmentioning

confidence: 99%

“…Light reflection of face image was also used in identification of spoof attacks. Mhou et al [12] proposed a method that employs night vision infrared cameras in detecting fraudulent biometrics. Along with the infrared cameras, LPB algorithms, blur detection, median blur filter and Gabor filters were used in the identification process.…”

Section: Introductionmentioning

confidence: 99%

The Effectiveness of Depth Data in Liveness Face Authentication Using 3D Sensor Cameras

Albakri

Alghowinem

2019

Sensors

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Even though biometric technology increases the security of systems that use it, they are prone to spoof attacks where attempts of fraudulent biometrics are used. To overcome these risks, techniques on detecting liveness of the biometric measure are employed. For example, in systems that utilise face authentication as biometrics, a liveness is assured using an estimation of blood flow, or analysis of quality of the face image. Liveness assurance of the face using real depth technique is rarely used in biometric devices and in the literature, even with the availability of depth datasets. Therefore, this technique of employing 3D cameras for liveness of face authentication is underexplored for its vulnerabilities to spoofing attacks. This research reviews the literature on this aspect and then evaluates the liveness detection to suggest solutions that account for the weaknesses found in detecting spoofing attacks. We conduct a proof-of-concept study to assess the liveness detection of 3D cameras in three devices, where the results show that having more flexibility resulted in achieving a higher rate in detecting spoofing attacks. Nonetheless, it was found that selecting a wide depth range of the 3D camera is important for anti-spoofing security recognition systems such as surveillance cameras used in airports. Therefore, to utilise the depth information and implement techniques that detect faces regardless of the distance, a 3D camera with long maximum depth range (e.g., 20 m) and high resolution stereo cameras could be selected, which can have a positive impact on accuracy.

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Face spoof detection using light reflection in moderate to low lighting

Cited by 8 publications

References 10 publications

Exponentiating the Precision of Facial Spoof Detection: An Intricately Woven Tapestry of Hybrid Machine Learning Paradigms and State-of-the-Art Textural Feature Extraction Methodologies

Exponentiating the Precision of Facial Spoof Detection: An Intricately Woven Tapestry of Hybrid Machine Learning Paradigms and State-of-the-Art Textural Feature Extraction Methodologies

A Face Spoofing Detection System Using Surf Analysis Based on Genetic Algorithm and Artificial Intelligence Technique.

The Effectiveness of Depth Data in Liveness Face Authentication Using 3D Sensor Cameras

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