Feature Guided Convolutional Neural Network for Face Recognition of NewBorn Babies

The latest technological advancements offer methods for identifying individuals, with facial matching or facial image recognition being prominent among them. This technology is widely utilized worldwide for various purposes. The primary objective is to establish the absolute and definitive identity of a newborn baby at the earliest possible and increase the recognition rate and decrease the false alarm rate during the recognition process of an infant. The implementation of face matching for babies serves as a preventive measure against incidents like exchange, mixing, and kidnapping, among others. It also holds significant importance in the field of medical science, addressing long-standing challenges. With a rising number of such cases worldwide, face matching emerges as a viable solution to mitigate these issues. The dataset for the newborn babies has been collected from the Mandya Medical College, Mandya for three continuous days once the baby is born. Preprocessing of Images is done to remove unwanted information. Convolutional Neural Network is used for face matching. This paper offers a comparison of different facematching techniques, aiming to assess their respective efficiency and accuracy within the system.