Background: Recognizing human faces across image processing is a difficult task mainly
when it comes to age variation and occluded images. Aging causes a lot of variation in the human face
and occlusion makes it difficult for us to recognize image of a person. Human faces undergo changes due
to aging. These changes are affected by different factors and are subject to different age groups. In the
early ages, like the childhood the facial shape is of importance and later on during the adulthood texture
variations like wrinkles and pigmentation is seen. Age variation brings a major problem to the systems
which recognize faces. Further it found that the task of identification is being complicated due to occlusions.
Recognizing faces under occlusion mainly consists of registration and classification and there is
very less work done in both of these areas.
Methods: In the paper, two novel techniques have been developed to recognize the human face which
varies across age and under occlusion. Recognizing faces across Age Variations was proposed using the
Sparse Representation technique. Recognizing faces under Occlusion was proposed using the Principal
Component Analysis (PCA) Extraction and 1- Nearest Neighbor (NN) Classification techniques.
Results: From the analysis with various existing state of the art techniques, it was found that the proposed
method to recognize faces across age variation using Sparse Representation Technique gives the best recognition
rate of 81.81% on FGNET database. Also recognizing faces under occlusion using Principal Component
Analysis extraction and 1-Nearest Neighbor Classification gives the best recognition rate of 95.890% on
IIITD Disguise face database. We have considered the patents ‘Three dimensional human face recognition
method based on the intermediate frequency information in geometry image’, ‘The face identification
method based on multiscale weber local descriptor and the kernel group sparse representation’.
Conclusion: In this paper, we have developed two novel approaches to recognize faces across age variations
and under occlusion. The two novel techniques developed were compared across various existing
state of the art techniques and validated across various standard public face databases. From our analysis
we have found that the two novel techniques give the best recognition rates across age progressions and