A method has been developed to track a region related to respiration process in thermal images. The respiration region of interest (ROI) consisted of the skin area around the tip of the nose. The method was then used as part of a non-contact respiration rate monitoring that determined the skin temperature changes caused by respiration. The ROI was located by the first determining the relevant salient features of the human face physiology. These features were the warmest and coldest facial points. The tracking method was tested on thermal video images containing no head movements, small random and regular head movements. The method proved valuable for tracking the ROI in all these head movement types. It was also possible to use this tracking method to monitor respiration rate involving a number of head movement types. Currently, more investigations are underway to improve the tracking method so that it can track the ROI in cases larger head movements.

An Evaluation of Thermal Imaging Based Respiration Rate Monitoring in Children

Iris-biometrics are an alternative way of authenticating and identifying a person because biometric identifiers are unique to people. This paper introduces a method aims to efficient human identification by enhanced iris detection method within acceptable time. After preparing various type of images, then perform a series of pre-processing steps and standardize them, after that use Uni-Net learning, so identify the human by Navie-Bays method is the last step based on the output of Uni-Net which is role as feature extractor for the iris part and another sub-net for non-iris part that may involve identification-outcome. The outcome of this method looked good compared to some high-level methods, so, was accuracy-rate 9855, 99.25, and 99.81 for CASIA-v4, ITT-Delhi, and MMU-database respectively. Also, this paper introduces a method of iris recognition using CNN model which is improved the preprocessed patterns that together from dataset applied some procedures to develop them based on techniques of equalization and acclimate contrast ones. After that characteristic extracted and classified using CNN that comprises of 10 layers with back-propagation schema and adjusted moment evaluation Adam-optimizer for modernize weights. The overall accuracy was 95.31% with utilization time 17.58 (mints) for training-model.

Tumor Detection and Extraction in the Human Brain