Face recognition based biometric authentication systems are being widely adopted but they are vulnerable to presentation attacks. Detecting presentation attack is important to enhance the security level of face recognition biometric systems. Many presentation attack detection systems (PAD) have been proposed based on comparison of real and presentation image features. But these solutions can be deceived easily by creating the exact replica of real face. To solve this problem, this work proposes a liveliness approach which solves PAD as a challenge response problem. The response of face to a challenge is measured and analyzed to detect PAD. The challenge response matching is realized using a novel Face action unit biased convolutional neural network which selectively skips feature learning in non action unit areas. This novel deep learning model speeds up the challenge response face matching, increases the accuracy of liveliness matching and robust against environmental distortionss.