Deep Partial Face Recognition Extracting Features and Age-Gender Prediction using VGG

Traditional face recognition systems often struggle when only partial facial information is available due to factors like occlusion, disguise, or low image resolution. To overcome these limitations, this project introduces a deep learning based platform for partial face recognition that eliminates the need for facial alignment and is able to infer gender and age from incomplete facial data. At the heart of the system is a customized VGG Convolutional Neural Network (CNN), specifically adapted for deep feature extraction from partial facial inputs. Designed for real-time operation, the system is well-suited for practical applications such as law enforcement and intelligent surveillance. Unlike conventional methods that depend on precise face alignment, our approach leverages robust spatial feature encoding to recognize individuals even when only segments of the face are visible. In addition to identity recognition, the system simultaneously performs age estimation and gender classification, enriching the recognition process with supplementary demographic information. These tasks are executed concurrently, showcasing the framework's efficiency and adaptability. Comprehensive evaluations on partial face datasets highlight the model's strong accuracy in identity recognition and its consistent performance in predicting gender and age.