Biometric is an automated recognition process that measures the unique human characteristics, such as iris recognition. Of late, different kinds of iris recognition techniques are available that can be used in different platforms. Daugman algorithm is one of the iris recognition techniques that provide high percentage of accuracy. This study aims to develop a device that performs Iris Recognition using Daugman algorithm on Raspberry Pi. Through the implementation of Daugman algorithm on Raspberry Pi, more applications may come up for iris recognition. Also to make the system adapt with the limitations of Raspberry Pi and the existing application, this work created an image processing function that preprocesses the image before it is passed to the application. Based on the tests implemented, the False Acceptance Rate (FAR) and False Rejection Rate (FRR), the system is mostly to reject registered actors than to accept impostors due to the hardware limitations.

This study focuses on the geometric features of skin lesions for detecting and classifying skin cancer. Geometric features of the skin lesion are extracted following the asymmetry, border, and diameter parameters of the ABCD-Rule of Dermoscopy. In particular, area, perimeter, circularity index, greatest and shortest diameter, irregularity index and equivalent diameter are the parameters loaded in the dataset for classification. Three classifications of skin lesion are considered in this study such as malignant melanoma, benign melanoma, and unknown. Classification of skin lesion images is done through k-Nearest Neighbors (k-NN) algorithm and shows an accuracy of 90.0% in the functionality testing.

This study focuses on digitizing the measurement of intermalleolar distance (IMD), intercondylar distance (ICD) and the detection of valgus (knock knees) and varus (bowlegs) disease. IMD is the distance between two ankles while ICD is the distance between two knees. These two measurements can be used to determine if a person has a related disease or not. This paper utilizes the YOLO algorithm and Holistically-Nested edge detection to detect the subject knees, ankles and its edges. The YOLO algorithm allows the training of the system enabling it to make the detection more accurate. On the other hand, the Holistically-Nested edge detection eliminates some pre-processing steps and making it more accurate detection in natural images. The Euclidean distance or Euclidean Metrics and the use of DPI for measuring IMD and ICD have been explored in this paper. The prototype has been able to measure the IMD and ICD distances from an image and the results of it outputs the related disease. The results are verified by independent-sample t-test and it shows that the results from both methods does not have a significant difference, which means this study can produce similar results to the traditional method of measuring IMD and ICD distances.