ABSTRACT

Biometric systems are quite common in our everyday life. Despite the higher difficulty to circumvent them, nowadays criminals are developing techniques to accurately simulate physical, physiological, and behavioural traits of valid users, process known as spoofing attack. In this context, robust countermeasure methods must be developed and integrated with the traditional biometric applications in order to prevent such frauds. Despite face being a promising trait due to its convenience and acceptability, face recognition systems can be easily fooled with common printed photographs. Most of state-of-the-art anti-spoofing techniques for face recognition applications extract handcrafted texture features from images, mainly based on the efficient local binary patterns (LBP) descriptor, to characterize them. However, recent results indicate that high-level (deep) features are more robust for such complex tasks. In this brief, a novel approach for face spoofing detection that extracts deep texture features from images by integrating the LBP descriptor to a modified convolutional neural network is proposed. Experiments on the NUAA spoofing database indicate that such deep neural network (called LBP net) and an extended version of it (n-LBP net) outperform other state-of-the-art techniques, presenting great results in terms of attack detection.

EXISITNG :

There are many points of attack in security systems that can be exploited by criminals. In the case of biometric systems, the great majority of attacks occur by fooling the capture sensor with synthetic traits since no knowledge regarding the inner working of the application is needed [4]. Among the main biometric traits, face is a promising one especially due to its convenience, low cost of acquisition and acceptability by users, being very suitable to a wide variety of environments, including mobile ones. However, despite all these advantages, face recognition systems are the ones that most suffer with spoofing attacks since they can be easily fooled even with common printed photographs obtained in the worldwide network.

PROPOSED SYSTEM:

In this brief a novel approach for face spoofing detection that works with high-level (deep) texture features instead of handcrafted ones is proposed based on a modified Convolutional Neural Network (CNN) [5] by incorporating the LBP (Local Binary Patterns) [6] texture descriptor in its first layer. Besides the good results of LBP itself, CNNs have been increasingly used in such difficult tasks since they can extract and work accurately with high-level features, learned from the own set of training data, being more robust and suitable for activities such as attack detection, in which patterns are complex and can not be easily detected. Experiments show that the proposed deep neural network, called LBP net, and its extended version, n-LBP net (normalized LBP Net), outperform the state of-the-art techniques based on handcrafted texture information, presenting great results in terms of attack detection.

SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10

HARDWARE REQUIREMENTS:

 Processor : Core I3
 RAM Capacity : 2 GB
 Hard Disk : 250 GB
 Monitor : 15″ Color
 Mouse : 2 or 3 Button Mouse
 Key Board : Windows 08/10