A robust anti-spoofing technique for face liveness detection with morphological operations

Singh, Manminder; Arora, Ajat Shatru

doi:10.1016/j.ijleo.2017.04.004

Cited by 20 publications

(13 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…FAR denotes the false acceptance rate and FRR is used for representing false rejection rate. ‘S’ is the face anti-spoofing dataset whereas the value of equal error rate (EER) is estimated on ‘P [ 32 , 33 , 37 , 42 , 60 , 63 , 65 – 71 , 73 , 76 , 79 – 82 , 84 – 86 , 98 , 99 , 110 , 111 , 118 , 121 – 123 , 125 – 127 , 129 , 132 , 135 , 135 , 139 , 141 , 147 , 160 ] EER At certain threshold when FAR = FRR The point where error rates are equal. Commonly FMR and FNMR are compared.…”

Section: Performance Evaluation Methodologiesmentioning

confidence: 99%

“…The proposed method is tested only on volunteer faces and not evaluated on standard datasets. Singh and Arora [ 111 ] used eye-blink and mouth movements for face liveness detection. The morphological opening operations are used to extract the abstract false regions in the consecutively captured frames from face video.…”

Section: State-of-the Art Face Pad Mechanismsmentioning

confidence: 99%

“…Research challenge 4. Generalization versus time in motion-based face PAD techniques: This survey clearly revealed that the motion-based face PAD methods ([ 33 , 102 – 111 ]) exhibits superior capabilities of generalization due to the cues and information of the entire sequence of video which are explored. However, in these methods, the whole sequence of video is required and this process results in higher time complexity.…”

Section: Open Research Challenges and Future Directionsmentioning

confidence: 99%

See 2 more Smart Citations

A survey on face presentation attack detection mechanisms: hitherto and future perspectives

Sharma

Selwal

2023

Multimedia Systems

View full text Add to dashboard Cite

The advances in human face recognition (FR) systems have recorded sublime success for automatic and secured authentication in diverse domains. Although the traditional methods have been overshadowed by face recognition counterpart during this progress, computer vision gains rapid traction, and the modern accomplishments address problems with real-world complexity. However, security threats in FR-based systems are a growing concern that offers a new-fangled track to the research community. In particular, recent past has witnessed ample instances of spoofing attacks where imposter breaches security of the system with an artifact of human face to circumvent the sensor module. Therefore, presentation attack detection (PAD) capabilities are instilled in the system for discriminating genuine and fake traits and anticipation of their impact on the overall behavior of the FR-based systems. To scrutinize exhaustively the current state-of-the-art efforts, provide insights, and identify potential research directions on face PAD mechanisms, this systematic study presents a review of face anti-spoofing techniques that use computational approaches. The study includes advancements in face PAD mechanisms ranging from traditional hardware-based solutions to up-to-date handcrafted features or deep learning-based approaches. We also present an analytical overview of face artifacts, performance protocols, and benchmark face anti-spoofing datasets. In addition, we perform analysis of the twelve recent state-of-the-art (SOTA) face PAD techniques on a common platform using identical dataset (i.e., REPLAY-ATTACK) and performance protocols (i.e., HTER and ACA). Our overall analysis investigates that despite prevalent face PAD mechanisms demonstrate potential performance, there exist some crucial issues that requisite a futuristic attention. Our analysis put forward a number of open issues such as; limited generalization to unknown attacks, inadequacy of face datasets for DL-models, training models with new fakes, efficient DL-enabled face PAD with smaller datasets, and limited discrimination of handcrafted features. Furthermore, the COVID-19 pandemic is an additional challenge to the existing face-based recognition systems, and hence to the PAD methods. Our motive is to present a complete reference of studies in this field and orient researchers to promising directions.

show abstract

Section: Performance Evaluation Methodologiesmentioning

confidence: 99%

Section: State-of-the Art Face Pad Mechanismsmentioning

confidence: 99%

Section: Open Research Challenges and Future Directionsmentioning

confidence: 99%

See 1 more Smart Citation

A survey on face presentation attack detection mechanisms: hitherto and future perspectives

Sharma

Selwal

2023

Multimedia Systems

View full text Add to dashboard Cite

show abstract

“…From a captured video sequence, physiological motion detection of estimating the blinking and extraction of the eye contour scheme was adopted to conduct efficient face spoofing detection. Through taking both eye blinks and mouth movements into consideration, a scheme based on morphological operations was presented in [26]. Moreover, combining motion cues and CNN, a new method has been proposed in recent research.…”

Section: Related Work Regarding Face Anti-spoofing Detectionmentioning

confidence: 99%

Face liveness detection: fusing colour texture feature and deep feature

et al. 2019

View full text Add to dashboard Cite

The identification which uses biological characteristics has been a current top in the recent past. However, numerous spoofing skills occur with the rising prosperity of advance recognition technology, especially in the detection and recognition of a face. In allusion to the problem above, more robust and accurate face spoofing detection schemes have been put forward. Convolutional neural networks (CNNs) have demonstrated extraordinary success in face liveness detection recently. In this study, an effective face anti-spoofing detection method based on CNN and rotation invariant local binary patterns (RI-LBP) has been proposed. First, the authors use CNN to extract deep features and use RI-LBP to extract colour texture features. In addition, the principal component analysis approach is employed to decrease the dimensions of deep characteristic. Moreover, two different features are fused before applying to support vector machine (SVM). Finally, the SVM classifier is adopted to identify genuine faces from fake faces. They have conducted extensive experiments to obtain a scheme of better generalisation capability for face anti-spoofing detection. The analysis results indicate that the proposed approach implements great generalisation capability over other state-of-the-art approaches within the intra-databases and cross-databases.

show abstract

“…In (11) conducted a challenge and response method for real-time liveness detection for security systems. In (12) proposed an eye and mouth movement combined technique for procuring maximum reliability during face liveness detection. In (13) proposed a hierarchical neural network, which can fuse image quality cues and motion cues for liveness detection.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Velocity Estimation Approach for Face Liveness Detection using Sparse Optical Flow Technique

Nanthini¹,

Puviarasan²,

Aruna³

2021

IJST

View full text Add to dashboard Cite

Objectives: To propose a new liveness detection algorithm using optical flow to ensure the presence of actual live face into a photograph or 2D masks in face recognition biometric security systems. Methods: This work proposes an antispoofing model namely Sparse Optical Flow Technique with Velocity Estimation Approach (SOFT_VEA). Optical flow is an effective method for tracking objects in motion. It is adapted in this work to capture facial movements and decide the liveness state. The proposed algorithm considers real faces and two kinds of photo imposters. Findings: From the input video, the motion information of specific facial landmark points is captured by an optical flow algorithm. Then, the velocity of those landmark points is estimated via Euclidean distance. Based on this calculated velocity, the fake face is discriminated from the real face using a threshold value. The Empirical study shows that the proposed face liveness detection model is effective with an accuracy of 88% and Half Total Error Rate (HTER) of 2.45. Novelty: The proposed work is based on real face and photo imposters. The liveness detection algorithm is developed with a novel velocity estimation approach. It is very helpful for biometric security systems.

show abstract

A robust anti-spoofing technique for face liveness detection with morphological operations

Cited by 20 publications

References 6 publications

A survey on face presentation attack detection mechanisms: hitherto and future perspectives

A survey on face presentation attack detection mechanisms: hitherto and future perspectives

Face liveness detection: fusing colour texture feature and deep feature

An Efficient Velocity Estimation Approach for Face Liveness Detection using Sparse Optical Flow Technique

Contact Info

Product

Resources

About