Abstract: This paper introduces a user-friendly face recognition system that includes liveness detection. The system's core programming is implemented in Python, which provides access to powerful machine learning libraries such as TensorFlow and Keras. These libraries are known for their high-level neural network capabilities, which greatly assist in training the dataset. In terms of hardware interfacing, a basic setup is employed to demonstrate the functionality of the liveness system. This setup incorporates a mechanism that automatically opens and closes a door when a recognizable face is detected. The face recognition system outlined in this paper aims to provide a straightforward and accessible solution for identifying individuals. By integrating liveness detection, it enhances security by verifying that the detected face is not a still image or a video playback. The choice of Python programming language for the core implementation offers several advantages. Python is widely used for machine learning tasks due to its simplicity and extensive libraries. TensorFlow and Keras, in particular, provide a comprehensive set of tools for neural network-based tasks, including facial recognition. Overall, this paper presents a practical and effective approach to face recognition, combining the power of Python and machine learning libraries to achieve accurate and reliable results while incorporating liveness detection for enhanced security applications.

 Keywords— liveness, neural networks, Biometrics, training, Python, ESP controller.

PDF | DOI: 10.17148/IJARCCE.2023.125118

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