• Paras Bhardwaj
• Aarti

This research is devoted specifically to the advancement of deep learning-based recognition using neural networks (CNN) and in the context of CelebA (facial expression) data. With the rapid development of deep learning algorithms, there is an interest in using them accurately and efficiently in many areas. The main purpose of this study is to determine the recognition model based on deep learning, to explore the complexity of the education process and to perform a well-done assessment. To achieve these goals, this study expands on various study designs, explores the complexity of the data collection process, and uses the situational assessment process to measure performance. Proposed through rigorous testing, the system demonstrates an exceptional ability to achieve high accuracy and robustness in real-world conditions. The implications of this research are particularly important in the knowledge of patterns. This research demonstrates the capabilities of deep learning algorithms, demonstrating their potential applications in many areas, including image recognition, speech recognition, and search results. The results of this study provide important insights into the field of smart authentication where accurate and reliable authentication is required. Overall, this research focused on the CelebA dataset and the use of CNNs, laying an important foundation for the advancement of deep learning-based recognition.

Deep learning, Face recognition, Neural Networks, Image Processing, Computer Vision

"Deep Learning Based Face Recognition System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 9, page no.d217-d228, September-2023, Available :http://www.jetir.org/papers/JETIR2309321.pdf

"Deep Learning Based Face Recognition System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 9, page no. ppd217-d228, September-2023, Available at : http://www.jetir.org/papers/JETIR2309321.pdf