Automatic face recognition system is an important component of intelligent human computer interaction systems for biometric. It is an attractive biometric approach, to distinguish one person from another. To perform Automatic face recognition system, the hybrid approach Wavelets face detection and Neural Network based Face Recognition is used. The face recognition accuracy is can be increased using a combination of Wavelet, PCA-FLD and Neural Networks. Preprocessing, feature extraction and classification rules are three crucial issues for face recognition. For preprocessing and feature extraction steps, we apply a combination of wavelet transform and PCA-FLD. During the classification stage, the Neural Network is explored to achieve a robust decision in presence of wide facial variations.

Skin malignant growth is quite possibly the most commonly seen Malignancy type in people. Skin disease happens because of the un controllable developing of transformations occurring in DNAs developing to certain reasons. Perceiving the malignant growth in beginning phases could build the opportunity of an effective treatment. These days, PC helped finding applications are utilized nearly at each field. From the real dermo scopic images, the first-stage network aims for precise segmentation of the skin lesion. The second-stage network is a classification network that can predict the existence of Melanoma and Squamous Cell Carcinoma in a skin sample. Deep convolutional neural networks, such as Inception-v4, ResNet-152, and DenseNet-161, were trained for melanoma and squamous cell carcinoma detection and seborrheickeratosis classification. U-Net with VGG-16 Encoder was trained to create segmentation masks for lesion segmentation. Resnet engineering achieves the highest precision of 90 percent among the equations used in the proposed models.