COMPARATIVE STUDY OF CNN RNN AND SVM BASED ON AGE AND GENDER DETECTION

ABSTRACT The advancement of artificial intelligence and computer vision has made facial analysis a key area of research, with applications ranging from security surveillance to personalized user experiences. This study presents a comparative evaluation of three machine learning and deep learning techniques: Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Support Vector Machine (SVM) for age and gender detection using facial images. The research aimed to determine which model provides superior accuracy, reliability, and computational efficiency for this classification task. Facial image datasets were collected and preprocessed through resizing, normalization, and augmentation to enhance model generalization. Each algorithm was trained and tested using the same dataset to ensure fair comparison. The performance of the models was evaluated using metrics including accuracy, precision, recall, and F1-score. Experimental results revealed that CNN achieved the highest accuracy and robustness, followed by RNN and SVM respectively. CNN’s layered architecture effectively captured spatial and structural features from facial images, making it the most suitable model for age and gender detection.The study concludes that deep learning approaches, particularly CNN, significantly outperform traditional classifiers such as SVM in complex image recognition tasks. The findings of this research contribute to the development of intelligent systems capable of accurate human attribute recognition and offer a foundation for future exploration of hybrid models and real-time facial analytics applications.