To perform a literature review to find-out current issues in biometrics and various liveness detection methodologies in different Biometric-Traits.
Automated LIVENESS Detection is very Hot topic in Biometrics research these days for different biometric traits. Each trait eg: Face Recognition, Thumb, Iris, lips, skin texture all have different methodologies to detect life in SUBJECT being Authenticated through Biometric-Device. On the other hand to find out open research areas in biometrics this short literature review is done.
Current Issues in Fingerprint Biometric Trait:
Fingerprint Authentication System can be attacked in following scenarios: 
- Direct Attacks: Fake Fingerprints (Spoofing)
- Example: Direct Attack with Cooperation
- Example: Direct Attack without Cooperation
- Brute Force indirect attacks
- Example: Brute Force attack to the feature extractor input
- Example: Brute Force attack to the matcher input
- Hill-Climbing indirect attacks
- Example: hill-climbing attack to the matcher input
- Example: hill-climbing attack to the feature extractor input
- Masquerade attacks
- Example: masquerade attack to the feature extractor input
- Example: masquerade attack to the sensor
Current Issues in Face Recognition:
Two Issues are still to be addressed:
- Illumination Problem
- Pose Problem
The illumination problem is illustrated in Figure below where the same face appears differently due to the change in lighting. More specially the changes induced by illumination could be larger than the differences between individuals causing systems based on comparing images to misclassify the identity of the input image.
The pose problem is illustrated in Figure where the same face appears differently due to changes in viewing condition. Moreover when illumination variation also appears in the face images the task of face recognition becomes even more difficult. Figure shows an analysis and classification of various pose problems are performed using a reflectance model with varying albedo
Current Issues in IRIS Recognition:
An iris recognition system is considered ideal when match and non-match distributions do not overlap each other. But there are a few factors which may lead to a significant drop in accuracy of iris recognition systems.
1) Dilation: One of the important but often ignored factor is pupil dilation. Due to dilation effects, we have varying size of pupil, which results in decreased recognition performance. Dilation may occur due to many factors such as drugs, sunglasses, light illumination, etc.
2) Lenses: Around the world, approximately 125 million people use contact lenses. Therefore, iris recognition systems should be flexible enough to accommodate these large number of people.
3) Twins: Data was collected on twins day festival Twinburg in Ohio in August 2009. After experiments, their findings indicate that there are similarities between the irises of genetically same users which can be visually identified, but current biometric systems do not identify them. [
4) Time Variability: Human iris is considered stable over time; but, a recent study by Gonzalez et al, shows results which contradict what has been demonstrated so far.
5) Cataract Surgery: Dhir et al. and Rakshit et al. identified the effects of cataract surgery on recognition performance.
Conclusion of IRIS Vulnerabilities:
This review paper  summarizes the issues and challenges with current iris biometric systems. In particular, security and performance related issues are discussed. It is shown that many popular beliefs about security, reliability, stability and performance of iris recognition systems are not correct and need to be revisited.
Different Ways of Liveness Detection In Different Biometric Traits
As we previously discussed, Liveness Detection is one of the biggest challenge in Biometric Research, Here some useful tips are collected from Up-To-Date literature review.
Liveness Detection Through Fingerprint Biometric:
- Active Sweat Pores Based Liveness Detection 
- Temperature Sensing 
- Detection of pulsation on fingertip 
- Pulse oximetry 
- Electrical Conductivity 
- ECG 
Liveness Detection in IRIS Recognition:
- Detecting Motions of Eye Retina 
- Detecting Reflection from Eye 
Detection of Reflection is very important as Dead Human’s Eye also does not give reflection
Liveness Detection in Face Recognition:
- Detecting Expressions
- Detecting Skin Dynamic Texture (Requires HDD Camera) 
Liveness Detection in Voice Recognition:
- Repeating randomly generated sequence of digits and phrases 
- Matching the lip movement (video) to the the audio. 
In this little survey we discussed various issues in today’s biometric’s research and challenges faced by researchers all over the world. And we also discussed LIVENESS detection techniques from various biometric traits.
It is concluded that by combining different methodologies (MultiModoal Biometrics) we can achieve greater performance and better Live Human Detection by combining multiple Life detection algorithms in a single device.
- Boatwright and X. Luo. What do we know about biometrics authentication? In InfoSecCD ’07: Proceedings of the 4th annual conference on Information security curriculum development, pages 1–5, New York, NY, USA, 2007. ACM.
- E. Boult and R. Woodworth. Privacy and Security Enhancements in Biometrics. Springer, US, 2005.
- K. Jain. Biometric Recognition: How Do I Know Who You Are? Springer, US, 2005
- Martinez-Diaz, J. Fierrez-Aguilar, F. Alonso-Fernandez, J. Ortega-Garcia, J.A. Siguenza, Hill-Climbing and Brute-Force Attacks on Biometric Systems: A Case Study in Match-on-Card Fingerprint Verification, ATVS/Biometrics Research Lab, Escuela Politecnica Superior – Universidad Autonoma de Madrid C/ Francisco Tomas y Valiente, 11 – Campus de Cantoblanco – 28049 Madrid, Spain
- Shahzad Ahmed Memon, Novel active sweat pores based liveness detection techniques for fingerprint biometrics, Brunel University School of Engineering and Design PhD Theses, 2012
- Tiago de Freitas Pereira, Jukka Komulainen, André Anjos, José Mario De Martino, Abdenour Hadid, Matti Pietikäinen and Sébastien Marcel, Face liveness detection using dynamic texture, Pereira et al. EURASIP Journal on Image and Video Processing 2014, 2014:2
- Javier Galbally, Jaime Ortiz-Lopez, Julian Fierrez and Javier Ortega-Garcia,Iris Liveness Detection Based on Quality Related Features, ATVS – Biometric Recognition Group, Universidad Autonoma de Madrid C/ Francisco Tomas y Valiente 11, 28049 Madrid. SPAIN.
- Christopher K. Boyce, Multispectral Iris Recognition Analysis:Techniques and Evaluation, Morgantown, West Virginia. 2006
- Martin Chrzan, Liveness detection for face recognition, MASARYK UNIVERSITY FACULTY OF INFORMATICS, MASTER THESIS
- Stephanie Schuckers, PhD, Larry Hornak, PhD, Tim Norman, PhD, Reza Derakhshani, Sujan Parthasaradhi, Issues for Liveness Detection in Biometrics, Center for Identification Technology Research, WEST VIRGINIA UNIVERSITY
- WenYi Zhao, Rama Chellappa, Imagebased Face Recognition Issues and Methods, WenYi Zhao was at the Center for Automation Research while this work was performed.
- Andy Adler, Can sample images be regenerated from biometric templates?, School of Information Technology and Engineering, University of Ottawa.
- German researcher reverse-engineers a fingerprint using photos, http://www.biometricupdate.com/201412/german-researcher-reverse-engineers-a-fingerprint-using-photos, 2015-05-25.
- Sajida Kalsoom, Sheikh Ziauddin, Iris Recognition: Existing Methods and Open Issues, The Fourth International Conferences on Pervasive Patterns and Applications, IARIA, 2012.