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Face biometric is becoming more popular because of its wide range of applications in authorizing the person either from an image or from the video sequence. The bottleneck in face recognition is Pose angle variation, varying light condition, Partial Occlusion, Blur in the image or Noise. The proposed method first removes the noise from the image using Adaptive Median Filter (AMF) then Discrete Cosine Transform(DCT) is applied to normalize the illumination problem. The algorithm is further used to remove the motion blur using Lucy Richardson’s method by calculating the Point Spread Function (PSF). The Pose variation problem is then addressed with Global Linear Regression(GLR). Then the Principal Component Analysis(PCA) and Linear Discriminant Analysis(LDA) are applied to the normalized image to get the feature vector. This combined feature score is used to recognize the image using K-Nearest Neighbor (K-NN). The result shows a maximum accuracy of 92% and 87.5% with Pose angle variation between (0°, 22°) and (22°, 40°) respectively. The pose variation greater than this shows an average accuracy of 77.5%. The result also shows a maximum computation speed of 0.018 Seconds.

The Biomedical image analysis technique used in most of the clinical diagnosis activities, which is one of the explorative areas that appeal intense significance among scientists. The retinal fundus images are utilized in clinical diagnosis extensively for the treatment and to observe various eye diseases. Diabetic retinopathy is one of the foremost sources for blindness. The major diagnostic sign of diabetic retinopathy is the damage of blood vessels due to various reasons in the eye and then establishment of lesions in the retina. The screening and detection of Diabetic Retinopathy can be performed using retinal fundus images. The identification and analysis of diabetic retinopathy (DR) by means of color fundus images involves experienced practitioners to recognize the existence of many small topographies with a detailed grading system, makes this a complex and time-consuming mission. In this paper, a novel systematized method for the discovery of exudates in retinal images to diagnose diabetic retinopathy. The color fundus images are characterized and analyzed to find microaneurysms on the retina and provides the severity. The algorithm is tested on datasets provided by ophthalmologists and Messidor dataset, which gave excellent and promising results.