Camera-based eye blinks pattern detection for intelligent mouse

Abstract

Human-computer interface systems provide an alternative input modality to allow people with severe disabilities to access computer systems. One of the inexpensive and unobtrusive methods for this purpose is image-based eye blinks detection. Currently, available human-computer interface systems are often intrusive, limit in head rotation, require special hardware, and have special lighting or manual initialization. This paper presented a new robust method for real-time eye blinks detection. This method enables interaction using "blink patterns," which are sequences of long and short blinks interpreted as semiotic messages. The precise location of the eye is determined automatically through multi-cues, accompanied by integration of eye variance feature and Gaussian Mixture Model classifier. The detected eye window is converted into a binary image. The eyelid's distance is extracted by applying a variance projection derivative function. By following the eyelid's distance in a finite-state machine, the blink patterns can be detected. The performance of the presented algorithm is evaluated using several frame streams. The experimental results show a robust eye blink pattern detection system in real environments.

Spintronic Sensors Based on Magnetic Tunnel Junctions for Wireless Eye Movement Gesture Control

“…The average accuracy of the other systems was greater than 90% with ITR greater than 56 bits/min. Some of these methods deliver a very high accuracy at a high transfer rate, but are too large and immobile for realistic wearable applications [67]- [72]. The system demonstrated by Graybill et al [73] involved developing smaller, more mobile, light insensitive and more inexpensive solutions.…”

Spintronic Eyeblink Gesture Sensor With Wearable Interface System

“…Comparison Table 1 modified from Tanwear et al [44], compares seven eyeblink studies and two studies that detect brain activity for human computer interaction (HCI) [18], [36], [41], [45]- [50]. The table has two metrics that compares the performance of the different types of HCI devices, accuracy and ITR.…”

Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review

“…Specifically, the time still is considered slow and inaccurate when it comes to approaches that do not use brain signals, and also the need for electrodes connected to the head of the patient [ 47 , 54 , 64 ]. Other approaches to human–computer interaction systems that do not necessarily involve brain signals by EEG can be seen in Pinheiro et al [ 65 ], Hori et al [ 66 ], Fathi et al [ 67 ], Harezlak et al [ 68 ], Villanueva et al [ 69 ], Królak e Strumiłło [ 24 ], Zhao et al [ 70 ], Liu et al [ 71 ] and Aharonson et al [ 22 ].…”