No abstract

Biometric face recognition is becoming more frequently used in different application scenarios. However, spoofing attacks with facial disguises are still a serious problem for state of the art face recognition algorithms. This work proposes an approach to face verification based on spectral signatures of material surfaces in the short wave infrared (SWIR) range. They allow distinguishing authentic human skin reliably from other materials, independent of the skin type. We present the design of an active SWIR imaging system that acquires four-band multispectral image stacks in real-time. The system uses pulsed small band illumination, which allows for fast image acquisition and high spectral resolution and renders it widely independent of ambient light. After extracting the spectral signatures from the acquired images, detected faces can be verified or rejected by classifying the material as “skin” or “no-skin.” The approach is extensively evaluated with respect to both acquisition and classification performance. In addition, we present a database containing RGB and multispectral SWIR face images, as well as spectrometer measurements of a variety of subjects, which is used to evaluate our approach and will be made available to the research community by the time this work is published.

Commercial light curtains use a technique known as muting to differentiate between work pieces and other objects (e.g., human limbs) based on precise model knowledge of the process. At manually fed machinery (e.g., bench saws), such precise models cannot be derived due to the way the machinery is used. This paper presents a multispectral scanning sensor to classify an object's surface material as a new approach for the problem. The system is meant to detect the presence of limbs and therefore optimized for human skin detection. Evaluation on a test set of skin and (wet) wood samples showed a sufficiently high reliability with respect to safety standards.

The FIVIS simulator system addresses the classical visual and acoustical cues as well as vestibular and further physiological cues. Sensory feedback from skin, muscles, and joints are integrated within this virtual reality visualization environment. By doing this it allows for simulating otherwise dangerous traffic situations in a controlled laboratory environment. The system has been successfully applied for road safety education applications of school children. In further research studies it is applied to perform multimedia perception experiments. It has been shown, that visual cues dominate by far the perception of visual depth in the majority of applications but the quality of depth perception might depend on the availability of other sensory information. This however, needs to be investigated in more detail in the future.