<![CDATA[Facial image processing technology is developing rapidly and is used in various fields, one of which is for human age group classification. As we age, the face experiences changes such as wrinkles, bone structure, and facial proportions. This recognition process faces challenges, such as variations in texture, lighting, expression, and fine wrinkles that are difficult to detect automatically. An optimal feature extraction method is needed to improve the accuracy of age group classification. This study aims to classify age groups based on facial images using a computer system, as well as to determine the accuracy in real time and photo input. The methods used are Gabor Filter and Histogram of Oriented Gradients (HOG) as feature extraction and Artificial Neural Network (ANN) as a classification algorithm. The system is designed to operate in real time and photo input, with fast and efficient classification results. The dataset consists of 2,500 facial images, divided into five age groups, each consisting of 500 images. A total of 50 images from each age group are used as test data. The system classifies images into five age groups, namely toddlers, children, adolescents, adults, and the elderly. The research results showed an accuracy of 74% for the real-time system and 76% for the photo input system.]]>
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