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We present an algorithm for the automatic recognition of facial features for color images of either frontal or rotated human faces. The algorithm first identifies the sub-images containing each feature, afterwards, it processes them separately to extract the characteristic fiducial points. Then Calculate the Euclidean distances between the center of gravity coordinate and the annotated fiducial points' coordinates of the face image. A system that performs these operations accurately and in real time would form a big step in achieving a human-like interaction between man and machine. This paper surveys the past work in solving these problems. The features are looked for in down-sampled images, the fiducial points are identified in the high resolution ones. Experiments indicate that our proposed method can obtain good classification accuracy.

Lung cancer is the prevalent malignancy afflicting both men and women, mostly affects the chain smokers. The lung CT images are examined to identifying the abnormalities, but diagnosing lung cancer with CT images is time-consuming and difficult task. In this work, a novel Sooty-LuCaNet has been proposed in which the best features are selected using sooty tern optimization to reduces computational complexity of neural network. Initially, the denoised CT images are segmented using Grabcut technique to separate the lung nodules by eliminating the background distortions. The deep learning based Shufflenet is used to extract the structural features from the segmented nodule and the textural features from the enhanced images. Afterwards, the sooty tern optimization (STO) algorithm is applied to select the most relevant features from the extracted features from the ShuffleNet. Finally, the classification process is carried out to differentiate the normal, small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) from the CT images. The experimental findings show the robustness of the proposed Sooty-LuCaNet based on the specific metrics namely sensitivity, accuracy, specificity, recall, precision and F1 score. An average classification accuracy of 99.16% is achieved for detection and classification of lung cancer.