Face Authentication using AI for Smart Verification of Passengers

Modernization of rail lines has always been an issue centered around the improvement of the major framework of a country. Since the rail routes address perhaps the best methods of transport offered to individuals, it is essential to keep a mind the security gives that are emerging in today’s world. As per the need there should be an up gradation in frameworks we use. One such up gradation is that the part of Artificial Intelligence and e-tagging that is accomplished with the help of face acknowledgment innovation. This innovation has been widely utilized as a biometric technique and subsequently can be utilized for traveler check.

IriTrack

With a growing adoption of face authentication systems in various application scenarios, face Presentation Attack Detection (PAD) has become of great importance to withstand artefacts. Existing methods of face PAD generally focus on designing intelligent classifiers or customized hardware to differentiate between the image or video samples of a real legitimate user and the imitated ones. Although effective, they can be resource-consuming and suffer from performance degradation due to environmental changes. In this paper, we propose IriTrack, which is a simple and efficient PAD system that takes iris movement as a significant evidence to identify face artefacts. More concretely, users are required to move their eyes along with a randomly generated poly-line, where the resulting trajectories of their irises are used as an evidence for PAD i.e., a presentation attack will be identified if the deviation of one's actual iris trajectory from the given poly-line exceeds a threshold. The threshold is carefully selected to balance the latency and accuracy of PAD. We have implemented a prototype and conducted extensive experiments to evaluate the performance of the proposed system. The results show that IriTrack can defend against artefacts with moderate time and memory overheads.

Automated Face Authentication and Alert System Using AI

Local animals such as buffaloes, elephants, goats, birds, and others frequently kill farm crops. Farmers lose a lot of money as a result. Farmers cannot barricade whole fields or remain on the premises 24 hours a day to guard them. For animal detection and unknown individual detection, we propose a deep learning process. We will create a system to detect wild animals trespassing on agricultural fields as part of this project. We'll be working on a device to identify wild animals trespassing on farmland as part of this project. Animal detection and classification may help farmers avoid damage to their fields, track down livestock, and avoid crop loss. To recognize unknown persons or animal, we'll use face recognition tools.

Face Recognition from Video using Threshold based Clustering

Video processing has gained significant attention due to the rapid growth in video feed collected from a variety of domains. Face recognition and summary generation is gaining attention in the branch of video data processing. The recognition includes face identification from video frames and face authentication. The face authentication is nothing but labelling the faces. Face recognition strategies used in image processing techniques cannot be directly applied to video processing due to bulk data. The video processing techniques face multiple problems such as pose variation, expression variation, illumination variation, camera angles, etc. A lot of research work is done for face authentication in terms of accuracy and efficiency improvement. The second important aspect is the video summarization. Very few works have been done on the video summarization due to its complexity, computational overhead, and lack of appropriate training data. In some of the existing work analysing celebrity video for finding association in name node or face node of video dataset using graphical representation need script or dynamic caption details As well as there can be multiple faces of same person per frame so using K- Means clustering further for recognition purpose needs cluster count initially considering total person in the video. The proposed system works on video face recognition and summary generation. The system automatically identifies the front and profile faces of users. The similar faces are grouped together using threshold based a fixed-width clustering which is one of the novel approach in face recognition process best of our knowledge and only top k faces are used for authentication. This improves system efficiency. After face authentication, the occurrence count of each user is extracted and a visual co-occurrence graph is generated as a video summarization. The system is tested on the video dataset of multi persons occurring in different videos. Total 20 videos are consider for training and testing containing multiple person in one frame. To evaluate the accuracy of recognition. 80% of faces are correctly identified and authenticated from the video.