Optimal feature-level fusion and layered k-support vector machine for spoofing face detection

A Contemporary Survey of Multimodal Presentation Attack Detection Techniques: Challenges and Opportunities

“…The authors also include class level liveness detection in their work. Kavitha et al [9] proposed a multimodal biometric framework that utilized feature level fusion to fuse the extracted features and support vector machine (SVM) classifier to detect the fake face.…”

“…These are categorized into two main parts reference-based and non-reference-based. In the reference-based method, an undistorted [26] Face Presentation attack Self-constructed Proposed a conditional random fields (CRFs) method Shahin et al [4] Hand vein and Fingerprint Spoof attack Self-constructed Proposed a new prototype design for multimodal biometrics Rodrigues et al [5] Face, Fingerprint Presentation attack FVC2004DB1, FERET-b series Proposed two novel schemes, the extension of the likelihood ratio based fusion and fuzzy logic Liting et al [28] Face Presentation attack Self-constructed Presented a face live detection method which utilized physiological motion Bao et al [16] Face Photo-attack Self-constructed Proposed optical flow field method to recognize the liveness of face Chetty [29] Face and voice Spoof attack VidTIMIT, DaFeX Corpora Proposed a new fuzzy fusion technique for liveness detection Komogortsev et al [30] Eye Spoof attack Self-constructed Proposed Oculomotor Plant Characteristics (OPC) based liveness detection method Barrero et al [7] Face, Iris Indirect Attack BioSecure Presented and evaluated the first software-based attack Singh et al [31] Face Spoofing attack Self-constructed Proposed a liveness detection measure to recognize and find the liveness of face, based on challenge and response method Akhtar et al [14] Face, iris fingerprint, Spoofing attack ATVS-Flr, ATVS-FFp Proposed mobile biometric liveness detection (MoBio LivDet) method Wild et al [42] Face and Fingerprint Direct attack LivDet 2013, CASIA FASD Proposed 1-median filtering as a spoofing resistant Das et al [8] sclera and iris Direct attack Self-constructed Proposed a framework for software-based liveness detection Boulkenafet et al [24] Face Presentation attack MSU-MFSD,CASIA-FASD Proposed an approach based on color texture analysis for face spoofing detection Lee et al [37] Finger-vein Spoof attack Self-constructed Proposed a finger-vein biometric recognition system based on image quality assessment Parveen et al [23] Face Presentation attack UPM, CASIA FASD, NUAA A dynamic local ternary pattern has been proposed which utilizes Weber's law Sollinger et al [40] Iris, face fingerprint, and finger vein Presentation attack ATVS-Flr, ATVS-FFp, IDIAP Proposed non-reference image quality measures (IQM) to differentiate between fake and real data Kavitha et al [9] Face Fake face attack CASIA FASD, MSU MFSD Proposed multimodal biometric framework to detect face spoofing Sahidullah et al [17] Voice Replay attack Self-constructed Proposed a body conducted sensor and throat microphone for automatic speaker verification reference image is utilized to estimate the quality of the test image while in the non-reference-based method pre-trained statistical models are utilized to ...…”

Detection of Fake Biometrics - Assessment of Image Quality in Face, Fingerprint

“…HVS is used for processing input images. This paper's Approach [9] is based upon the fact that digital media hide information by altering signal properties to introduce some degradation. This paper shows how adding a message or watermark to a digital media file can create unique artifacts that are detectable using Image Quality Measures.…”