FACE RECOGNITION SYSTEM

Written by: Anusha N (1st year MCA)

ABSTRACT

Face recognition technology, driven by artificial intelligence and machine learning, has made significant progress in recent years, transforming various aspects of our lives. This provides an overview of the state-of-the-art in face recognition, covering the technical advancements and ethical considerations associated with this technology. The technical aspect explores the use of deep learning techniques, particularly convolutional neural networks (CNNs), in achieving remarkable accuracy in identifying and verifying individuals based on their facial features. The applications are diverse, spanning security, access control, authentication, and law enforcement.

KEYWORDS - Convolutional neural networks (CNNs), Machine Learning.

INTRODUCTION

Humans have been using physical characteristics such as face, voice, gait, etc. to recognize each other for thousands of years. With new advances in technology, biometrics has become an emerging technology for recognizing individuals using their biological traits. This technology makes use of the fact that each person has specific unique physical traits that are one’s characteristics which can’t be lost, borrowed or stolen. By using biometrics it is possible to confirm or establish identity based on “who the individual is”, rather than by “what the individual possesses”. Several systems require authenticating a person before giving access to their resources. Biometrics has been long known to recognize persons based on their physical and behavioural characteristics.

Examples of different biometric systems include:

Fingerprint recognition, face recognition, iris recognition, retina recognition, hand geometry, voice recognition, signature recognition …etc.

METHODOLOGY :

Automated Face recognition in particular, has received a considerable attention in recent years both from the industry and the research communities. Automated face recognition is an interesting computer vision problem with many commercial and law enforcement applications. Mugshot matching, user verification and user access control, crowd surveillance, enhanced human computer interaction all become possible if an effective face recognition system can be implemented.

•Face Detection: The ultimate goal of the face detection is finding an object in an image as a face candidate that its shape resembles the shape of a face.

•Feature Extraction: Feature extraction abstracts high level information about individual patterns to facilitate recognition. Selection of feature extraction method is probably the single most important factor in achieving high recognition performance. To design a face recognition system with low to moderate complexity the feature vectors should contain the most pertinent information about the face to be recognized.

• Classifier: Comparison of the face to a database of known faces.

CASE STUDY

The case study illustrates how face recognition technology can be effectively employed to enhance campus security, access control, and attendance tracking in an educational institution. Despite challenges related to privacy and technical issues, the overall benefits in terms of security and convenience were substantial, making the technology a valuable addition to the university's infrastructure.

CONCLUSION

Face recognition systems using AI have the potential to revolutionize security, access control, and authentication across a wide spectrum of applications. However, they must be deployed responsibly, with strict adherence to privacy and ethical considerations. Ongoing research and development are necessary to address challenges related to bias, security, and accuracy, ensuring that these systems are both efficient and equitable in their use. As the technology evolves, striking the right balance between utility and ethics remains a fundamental goal for the development and deployment of face recognition systems.

REFERENCES

 https://ieeexplore.ieee.org/document/9057570.

 IRJET Journal