Face Spoof Detection Using Convolutional Neural Networks

Abstract

In recent years, the use of facial recognition technology has become increasingly prevalent, finding application in various areas, including security, authentication, and access management. With the extensive employment of face recognition technology has come an increase in the prevalence of face spoofing cases, wherein offenders manipulate the system with unauthentic facial information. The emergence of this issue poses a major risk to the dependability and protection of facial recognition technology. This calls for the development of advanced and robust techniques to detect face spoofing effectively. This thesis suggests a technique that employs convolutional neural networks (CNN) to identify fraudulent facial manipulation. The proposed method comprises teaching an intricate neural network using a comprehensive compilation of genuine and fabricated facial images. Two streams are employed in this process. RGB images are transformed to grayscale images in the first stream, and then facial reflection features are extracted. Face color features from RGB images are extracted in the second stream. These two characteristics are then combined and utilized to identify face spoofing. The structure of CNN includes several layers of convolution and pooling, which enable it to identify distinguishing features in the input images. Following its training, the model is employed to differentiate a presented facial image into either authentic or fraudulent. To determine the efficacy of the proposed technique, I employ a standardized data set for identifying counterfeit or altered facial attributes. The proposed approach has the capability to achieve an average precision rate of 89% while being applied to the provided data set. The suggested method presents various benefits compared to current techniques for detecting face spoofing. To start with, utilizing a deep CNN empowers the model to acquire intricate and discerning characteristics from the input images, thus augmenting the precision of the categorization mission. Additionally, the suggested method is effective in terms of computational requirements, enabling its utilization in real-time scenarios. The proposed methodology is able to withstand a range of fraudulent tactics used on facial recognition systems, such as print and replay attacks. The findings from this study aid in the progression of face recognition technology by enhancing the accuracy and dependability of fraud detection systems. These improved systems have practical applications in security measures, biometric identification, and digital criminal investigations. The suggested method could substantially enhance the dependability and safety of facial recognition systems, consequently boosting their functional value and credibility.