Face Recognition in Images with Missing Content using SVM for Vehicle Security

Face Recognition has been an excellent advancement to the present-day technology and is used in many aspects. This paper presents a model for face recognition using Support Vector Machines algorithm that recognizes the face of a person with the missing content from images. The main aim of the proposed model is to recognize a face of the driver and compares it with the images available from the dataset and used for unlocking the vehicle and give access to drive it. Then, it unlocks the steering if the correct face is matched. In this way, one can improve the vehicles’ protection from being theft. This recognition technique is included in the real time project and provided the outcomes with higher accuracies. The extension of this proposed model has also been included which uses fingerprints as one more recognition technique. This model describes the methodology and components that have been used and the experiments show that the proposed model is working with high accuracy.