<span lang="EN-US">There are some medicines and medical treatments that need to be injected into the human body through the blood vessels, and this requires placing the cannula in the patient’s body. The blood vessels in the human body differ from one person to another, and medical personnel face major problems in finding the blood vessels in most cases, because of The difference in skin color, where it is difficult to see the blood veins in the skin with black pigment, and it is difficult to find it in people with obesity because of the layers of fat, and in children and newborns because these veins are small. This study talks about finding a way to photograph these veins, see them by design and implementation low cost prototype used equipment that were recycled old device, such as a web camera, infrared lamps and overhead device, All of these devices are of low cost. Then process these images using binary image, histogram equalization, segmentation and threshold to detect these blood veins. The algorithms for edges images detecting are many and complex, this study used five methods to detect vein image, such as Sobel, Laplacian, Canny, Roberts, and Prewitt.</span>

<span lang="EN-US">Biometrics became fairly important to help people identifications persons by their individualities or features. In this paper, gait recognition has been based on a skeleton model as an important indicator in prevalent activities. Using the reliable dataset for the Chinese Academy of Sciences (CASIA) of silhouettes class C database. Each video has been discredited to 75 frames for each (20 persons (10 males and 10 females)) as (1.0), the result will be 1,500 frames. After Pre-processing the images, many features are extracted from human silhouette images. For gender classification, the human walking skeleton used in this study. The model proposed is based on morphological processes on the silhouette images. The common angle has been computed for the two legs. Later, principal components analysis (PCA) <em></em>was <em></em>applied <em></em>to <em></em>reduce <em></em>data <em></em>using <em></em>feature <em></em>selection <em></em>technology <em></em>to <em></em>get <em></em>the <em></em>most <em></em>useful <em></em>information in <em></em>gait <em></em>analysis. Applying two classifiers artificial neural network (ANN) and Gaussian Bayes to distinguish male or female for each classifier. The experimental results for the suggested method provided significant accomplishing about (95.5%), and accuracy of (75%). Gender classification using ANN is more efficient from the Gaussian Bayes technique by (20%), where ANN technique has given a superior performance in recognition.</span>