Is spectral reflectance of the face a reliable biometric?

Abstract

Over a decade ago, Pan et al. [IEEE TPAMI 25, 1552] performed face recognition using only the spectral reflectance of the face at six points and reported around 95% recognition rate. Since their database is private, no one has been able to replicate these results. Moreover, due to the unavailability of public datasets, there has been no detailed study in the literature on the viability of facial spectral reflectance for person identification. In this study, we introduce a new public database of facial spectral reflectance profiles measured with a high precision spectrometer. For each of the 40 subjects, spectral reflectance was measured at the same six points as Pan et al. [IEEE TPAMI 25, 1552] in multiple sessions and with time lapse. Furthermore, we sample the facial spectral reflectance from two public hyperspectral face image datasets and analyzed the data using state of the art face classification techniques. The best performing classifier achieved the maximum rank-1 identification rate of 53.8%. We conclude that facial spectral reflectance alone is not a reliable biometric for unconstrained face recognition.

An Effective Feature Segmentation Algorithm for a Hyper-Spectral Facial Image

“…morphological reconstruction and membership filtering (FRFCM) is proposed by Tao et al [31], that is significantly faster and more robust than FCM.…”

“…Finally, the performance of the proposed algorithm on PolyU-HSFD is compared with the previously mentioned standard FCM, spatial FCM, FRFCM and basic clustering. In Figure 10a (front facial image), the standard FCM clustering loses numerous features, because spectral reflectance of the face is not a reliable biometric, as spectral reflectance will change slightly due to the external environment [31]. Figure 10b illustrates spatial FCM clustering that performs better than the standard FCM algorithm as it makes full use of the spatial and spectral information, as expressed by Equation 7.…”

“…Each data cube of a hyper-spectral facial image contains 33 bands covering the visible spectral range from 400 to 720 nm with a 10 nm step. The SNR in this database is relatively lower because Uzair et al [31] used a novel algorithm that automatically adjusted the camera exposure time based on the filter's transmittance, illumination intensity and CCD sensitivity for each frequency band. Most subjects had slight head movements and eye blinking during image collection process, therefore, there was alignment errors between individual bands.…”

An extensive review on spectral imaging in biometric systems: Challenges & advancements

“…Uzair et al [40] also conducted experiments to verify if the spectral reflectance profile of face really made a reliable biometric for individual identification, based on measurements. These measurements were obtained using a high precision spectrometer.…”

Current Advances in Hyperspectral Face Recognition

“…Recently, many attempts have been made to use hyperspectral images for face recognition [24], [26], [28]- [31]. In this paper, We present a comprehensive literature review of hyperspectral face recognition methods.…”