A Low Cost Pupillometry Approach

A Low Cost Pupillometry Approach

Sergios Petridis (National Center for Scientific Research “Demokritos”, Greece), Theodoros Giannakopoulos (National Center for Scientific Research “Demokritos”, Greece) and Constantine D. Spyropoulos (National Center for Scientific Research “Demokritos”, Greece)
DOI: 10.4018/978-1-5225-9863-3.ch037
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The need for low-cost health monitoring is increasing with the continuous increase of the elderly population. In this context, unobtrusive audiovisual monitoring methods can be of great importance. More particularly, the diameter of the pupil is a valuable source of information, since, apart from pathological cases, it can reveal the emotional state, the fatigue and the ageing. To allow for unobtrusive monitoring to gain acceptance, one should seek for efficient methods of monitoring using common low-cost hardware. This paper describes a method for monitoring pupil sizes using a common, low-cost web camera in real time. The proposed approach detects the face and the eyes area at first stage. Subsequently, optimal iris and sclera location and radius, modeled as ellipses, are found using efficient spatial filtering. As a final step, the pupil center and radius is estimated by optimal filtering within the area of the iris. Experimental results show both the efficiency and the effectiveness of our approach.
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Face and Eye Bounding Box Detection

The overall scheme of the proposed method is presented in Figure 1. At a first stage, the face is detected. Face detection is a well-studied problem in machine vision (Viola and Jones, 2001) and there exist now several commercial tools that achieve high accuracy with high speed. For our purposes, we have used SHORETM (Sophisticated High-speed Object Recognition Engine) which achieves face detection at a frame rate greater than 50fps.

Figure 1.

The overall architecture of the proposed method


The same engine, also provides directly as a rough estimate of the two eyes area, which we have used to initiate iris and pupil detection.

Figure 2.

Sample eye area image


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