Realtime human face tracking and recognition system on uncontrolled environment

Recently, one of the most important biometrics is that automatically recognized human faces are based on dynamic facial images with different rotations and backgrounds. This paper presents a real-time system for human face tracking and recognition with various expressions of the face, poses, and rotations in an uncontrolled environment (dynamic background). Many steps are achieved in this paper to enhance, detect, and recognize the faces from the image frame taken by web-camera. The system has three steps: the first is to detect the face, Viola-Jones algorithm is used to achieve this purpose for frontal and profile face detection. In the second step, the color space algorithm is used to track the detected face from the previous step. The third step, principal component analysis (eigenfaces) algorithm is used to recognize faces. The result shows the effectiveness and robustness depending on the training and testing results. The real-time system result is compared with the results of the previous papers and gives a success, effectiveness, and robustness recognition rate of 91.12% with a low execution time. However, the execution time is not fixed due depending on the frame background and specification of the web camera and computer

Satellite Image Classification Using Moment and SVD Method

The motivation we address in this paper is to classify satellite image using the moment and singular value decomposition (SVD) method; both proposed methods are consisted of two phases; the enrollment and classification. The enrollment phase aims to extract the image classes to be stored in dataset as a training data. Since the SVD method is supervised method, it cannot enroll the intended dataset, instead, the moment based K-means was used to build the dataset. Thereby, the enrollment phase began with partitioning the image into uniform sized blocks, and estimating the moment for each image block. The moment is the feature by which the image blocks were grouped. Then, K-means is used to cluster the image blocks and determining the number of cluster and centroid of each cluster. The image block corresponding to these centroids were stored in the dataset to be used in the classification phase. The results of enrollment phase showed that the image contains five distinct classes, they are; water, vegetation, residential without vegetation, residential with vegetation, and open land. The classification phase consisted of multi stages; image composition, image transform, image partitioning, feature extraction, and then image classification. The SVD classification method used the dataset to estimate the classification feature SVD and compute the similarity measure for each block in the image, while the moment classification method used the dataset to compute the mean of each column and compute the similarity measure for each pixel in the image. The results assessment was carried out on the two classification paths by comparing the results with a reference classified image achieved by Iraqi Geological Surveying Corporation (GSC). The comparison process is done pixel by pixel for whole the considered image and computing some evaluation measurements. It was found that the classification method was high quality performed and the results showed acceptable classification scores. In the SVD method, the score was about 70.64%, and it is possible to rise up to 81.833% when assuming both classes: residential without vegetation and residential with vegetation are one class.Whereas, the classification score was about 95.84% when using the moment method. This encourage results indicates the ability of proposed methods to efficient classifying multibands satellite image.

Classification of Satellite Images Based on Color Features Using Remote Sensing

The aim of this paper is to classify satellite imagery using moment's features extraction with K-Means clustering algorithm in remote sensing. Clustering is the assignment of objects into groups called clusters so that objects from the same cluster are more similar to each other than objects from different clusters. In this research, the study area chosen is to cover the area of Baghdad city in Iraq taken by landsat 8. The proposed work consists of two phases: training and classification. The training phase aims to extract the moment features (mean, standard deviation, and skewness) for each block of the satellite imagery and store as dataset used in classification phase to compute the similarity measurement.  The experimental result of classification showed that the image contains five distinct classes (rivers, agriculture area, buildings with vegetation, buildings without vegetation, and bare lands). The classification result assessment was carried out by comparing the result with a reference classified image achieved by Iraqi Geological Surveying Corporation (GSC). It is observed that both the user accuracy and producers' accuracy and hence overall classification accuracy are enhanced with percent 92.12447%