How the Intelligent Recognition Industry Service (IRIS) Center is driving technological research in Artificial Intelligence

The Intelligent Recognition Industry Service (IRIS) Center, which is part of National Yunlin University of Science and Technology (YunTech), Taiwan, connects industry and academia in order to develop artificial intelligence (AI) solutions for pressing challenges in industrial automation, healthcare and industrial living. Professor Chuan-Yu Chang is Director of IRIS and Distinguished Professor at the Department of Computer Science and Information Engineering, YunTech. IRIS has four strategic principles: its human resource strand, international strategy, industrial strategy and technology pillar. Key foci centre on advancing international collaborations, introducing intelligent recognition technology within industry, promoting the use of AI and prompting its core technologies. Via its industrial service process, which begins with the identification of an industrial requirement and flows from diagnosis and resource matching, through to the provision of a customised service and problem resolution through the implementation and upgrade of technology, IRIS offers the expertise of its academic research staff to companies with a view to solving industry problems. IRIS is a leader in R&D in Taiwan and beyond, with particular strength in intelligent recognition technology, integrating sound recognition, medical imaging, UAV image recognition and vision inspection. The Centre seeks to advance R&D in these areas in order to improve lifestyles and workflow within Taiwan and highlight IRIS's status as a powerhouse in AI research on a global stage. An example of technology being developed at IRIS is the 'Infant Crying Translator' project, in which Chang and his team are using baby cry recognition technology and have launched the world's first baby cry recognition app to help parents understand the meaning of their baby's cries.

A Multi-Modal Gaze Tracking Algorithm

A person’s emotional information, needs and cognitive processes can be described by eye movement states and concerns, so gaze tracking was first applied in the field of psychology. With the continuous development of information technology, the application range of gaze tracking has expanded from psychology to medical, military, commercial and many other fields. Aiming at the problem of high misjudgment rate and long time-consuming of traditional iris location methods, this paper proposes a gaze tracking method based on human eye geometric characteristics to improve the tracking accuracy in 2D environment. First, the human face is located by face location algorithm and the position of human eye is estimated roughly. Then the iris template is built by iris image, and the iris center location algorithm is used to locate the iris center position. Finally, the eyes corners and iris center points are extracted to locate the eye area accurately and obtain the binocular image. The binocular images are input into the feature extraction network as multi-modal information in parallel, and the convoluted feature channels are reconstructed using the weight redistribution module in the network. Then the reconstructed features are fused in the full connection layer. Finally, the output layer is used to classify the reconstructed features. Experiments were carried out on a self-built screen block dataset. For 12 classified data, the lowest recognition error rate is 5.34%.

A New Approach for Sclera Segmentation Using Integro Differential Operator

Sclera vessel patterns are one of the most reliable biometrics against performance, accessibility and availability. Sclera segmentation is the important phase of sclera recognition system as its performance depends on the segmentation accuracy. The big challenge in segmentation is nonlinearity of iris, sclera and upper and lower eyelid. This paper proposes a new approach to isolate the sclera accurately from the unconditionally acquired eye image. Hough transform and Integro differential operator (IDO) are popularly used to confine the iris and sclera boundary. Both of these two localizations are done based on the midpoint of the iris. These techniques produce better results as compared with other approaches but computation of iris center point incurs more time. To overcome this time consumption issue gabor based filter has been used. Experimental results are listed based on the UBIRIS database images. The experimental result shows that the proposed segmentation system will produce fast and accurate result.

Detection of Iris Localization in Facial Images Using Haar Cascade Circular Hough Transform

In numerous science applications, face detection and iris extraction have been recognized as crucial stages by getting more consideration among researchers as it has an important job. This paper presents an automatic detection method of the iris and its center detection by applying the Haar Cascade Classifier and the Circular Hough Transform algorithm. The suggested method is divided into two primary methodologies: face recognition utilizes the Haar Cascade Classifier and iris extraction using the Hough Transform. The system detects the face from a set of facial images using an Impa-faced dataset. The improved AdaBoost algorithm constructs a cascaded classifier for face detection. Then, by applying the Haar Cascade to obtain an eye pair region and a Hough transform for iris detection by extracting Haar features. Finally, the improved circular Hough transform algorithm locates the iris center. The experimental results of the suggested method show a high-speed, robust ability to acquire the coordinates of the iris center accurately under various illumination changes on different states of human images. The overall accuracy for locating the iris center was 98.75%.

Open Educational Resources From the Innovative Resources for Instructional Success Center

Since their launch in 2002, open educational resources (OER) developed by the Innovative Resources for Instructional Success, better known as the IRIS Center, have become a staple of teacher preparation programs. In the spring of 2019, a survey of users revealed a diversity of ways teacher educators incorporate IRIS Center OERs within their preparation programs. This article describes these innovative applications and presents a snapshot of who IRIS users are and which IRIS Center OERs are most frequently used.