The document summarizes and compares different methods for face recognition, including Eigenface, Line Edge Map (LEM), and other techniques. It provides descriptions of how each technique works, such as using eigenvectors to extract features for Eigenface. Experimental results show LEM achieves better accuracy than Eigenfaces for variations in lighting and size. While Eigenfaces struggles with size changes, LEM maintains high accuracy for different conditions. The document recommends future work combining techniques to maximize recognition accuracy.

1. INFAREC Intelligent Face Recognition

4. Introduction To INFAREC

11. Architecture Of INFAREC

14. Architecture Of INFAREC Contd.. Input Phase Recognition Phase Knowledge Representation

17. Pattern Recognition

19. Face Recognition

21. First-order features values

22. Second-order features values

25. Eigen Face Algorithm

30. Eigen Face Algorithm contd.. (a) (b) Figure 2.1 (a) Sample, training set face images. (b) Average face image of the Training set.

32. Line Edge Map

34. Line Edge Map Algorithm

36. Line Edge Map Algorithm

38. Line Edge Map Algorithm

39. RESULTS

43. C onclusion

47. Future Enhancements And Recommendations

49. Other Techniques at a Glance

52. References

55. THANK YOU