Face Detection based on Haar Cascade and Convolution Neural Network (CNN)

Abstract

Face detection plays a crucial role in identifying individuals during suspicious activities, serving as a foundational component in various security applications. Modern face detection systems leverage machine learning algorithms to accurately identify human faces in images or videos, facilitating authentication in security contexts. This paper presents an innovative face detection system that integrates the Haar cascade method with Convolutional Neural Networks (CNNs), aimed at enhancing the accuracy of facial detection. The evaluation of the proposed system was carried out in a Python environment, utilizing real images from well-established public datasets, including Faces94, Faces95, Faces96, and the Grimace dataset, curated by Libor Spacek. The results obtained demonstrate the efficacy of the integrated approach, achieving accuracy rates of 98.37%, 97.22%, 97.52%, and 100% for the Faces94, Faces95, Faces96, and Grimace datasets, respectively. These findings indicate that the combination of Haar cascade and CNN-based methodologies significantly outperforms traditional machine learning face detection techniques, underscoring the potential for improved accuracy in real-world face detection applications. This research contributes to the ongoing advancements in facial recognition technology, with implications for enhanced security measures and intelligent human-computer interaction.