Coal_MTCNN: Coalesed Multi Task CNN with residual network extractor for face liveness detection in biometric application


  • M. Leelavathi, D. Kannan


face live detection, residual network, convolution neural network, feature extraction, biometric application


Face liveness detection is the first step of the entire face detection technology, and it is critical to system security when using face identification technology. Biometrics has become a fascinating yet tricky field in the past ten years. Although one of the most hopeful biometrics methods is facial detection, it is susceptible to fake attacks. To shield biometric verification systems from false assaults using printed photographs, video recordings, etc., many academics concentrate on facial liveness detection. As a result, the Coalesced Multi Task Convolution Neural Network (Coal_MTCNN) is used in the present research. The appropriate characteristics are first compressed and extracted from the leftover with the attention layer using dimensionality reduction before being sent to the encoder. The encoder uses the residual network design to maximize model size. Three distinct convolutional layers merge concurrently in the centre of each residual network to gather extra information. The encoder is upsampled in the decoder to translate the input images pixel for pixel into the segmented output. The residual Attention Network is constructed explicitly by layering attention modules that produce attention-aware characteristics. The proposed Coal_MTCNN is examined regarding various factors on two datasets, including FDDB and WIDER FACE. It is discovered that it obtains 99.6% accuracy for FDDB and 98% accuracy for WIDER FACE.


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How to Cite

M. Leelavathi. (2024). Coal_MTCNN: Coalesed Multi Task CNN with residual network extractor for face liveness detection in biometric application. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 2385–2393. Retrieved from



Research Article