Multi View Face Detection in Cattle Using Infrared Thermography Face detection in thermal imaging has been used widely in human for different purposes such as surveillance, obtaining physiological reading: respiratory and heart rate from face region via thermal imaging. Physiological reading via infrared thermal imaging used in emotion and stress detection as well as polygraph analysis. In animal as general and cattle in specific, face region localized manually in order to obtain temperature for eyes, nose and mouth, which used for stress, diseases and inflammation detection. In order to develop a future automated system for monitoring health conditions in cattle, it required to detect the face region automatically. Based on author knowledge, there is no research done regarding face detection in cattle using infrared thermal images. Unlike the human, cattle keep roaming, which lead to a change in the face and body orientation. The main objective of this paper is proposing a new method for Multiview face detection in cattle with accuracy enhancement by using three classifiers and temperature thresholding. Classifiers are established by using Histogram Oriented Gradient (HOG) as features and Support vector machine (SVM) for classification. The results show that the proposed algorithm is performing well in term of Specificity, Recall and Fmeasure and detection rate compare to the currently used method in the literature.

Multiperson remote heartrate measurement from human faces  a CNN based approach The conventional heart rate measurement techniques using electrocardiogram, wrist belts and so on, are attached with some disadvantages like discomfort, irritation of the skin and even more. From this starting point various video based heart rate measurement techniques were developed in the last years. All of these techniques use video data of the human faces to extract information to estimate the heart rate of the person in front of the camera. Most of them work with the detection of minimal changes in the color spectrum of the recorded video data. With these extracted information and multiple concluding steps, they are able to estimate the heart rate of the person sitting in front of the camera. All of these approaches have some big disadvantages, they are only able to measure the heart rate of one person  and in a small number of cases up to three persons. So the main objective of this work is to adapt and enhance existing research approaches of processing visual biological signals. The focus is set on the design and deployment of a new system, which is able to process visual data as well as detect and localize human faces by using artificial neural networks and calculate each individuals heart rate from the processed dataxe2x80x99s information. For the first time in this research area, different scenarios with more than three observed participants are being analyzed to calculate the heart rate for each of them separately. It will be shown that by using the applied technical architecture and technologies, it is possible to deploy a comparable and qualitatively competitive system. In addition, the implemented system proves the feasibility of processing various facesxe2x80x99 signals and calculating the respective heart rates simultaneously within an acceptable range of tolerance.