ON THE REMOTE DETECTION OF HUMAN SKIN SIGNATURES

Every year, search and rescue operations are employed to sav e numerous lives all over the world. In order to achieve this goal, the agencies responsible for these operations strive to reduce the time to find people who are lost or in distress. Th is is a particularly challenging task, specially when the search is performed on vast and complex environments such as open oc ean waters, deserts, mountains, forests and flooded regions. It u ually involves visual screenings primarily performed by personnel onboard low-flying aircrafts [1]. During long airborne sear ches, the performance of the human operators may degrade due to fatigue, which may result in vital target clues to be missed [ 2]. The fundamental importance of reducing search time and incr easing the probability of successful rescues have motivate d the development of sophisticated airborne detection syste ms equipped with multispectral and hyperspectral sensors ( e.g., [3, 4]). Due to their real-time capabilities and relatively low cost compared with other technologies, such as synthetic ap erture radar [2], these systems have become essential tools for sea rch and rescue operations at sea and on land [1, 3]. Installed on aircrafts operating at relatively high altitudes, they pro vide information that complements the visual screenings pe rformed at low altitudes, resulting in faster and more complete scene c ov rage [2]. Despite the significant advances in this area, however, the e ffective detection of human targets remains an open problem , notably in environments composed of background materials c haracterized by spectral features that pose limited contra st with spectral signatures of human skin. Such limited contrast ma y result in false positives, or false alarms, during time-cr itical search and rescue operations. These situations may hinder the chan ces of survival of a lost individual, especially under adver se environmental conditions, since valuable time may be unduly emp loyed in their investigation. Accordingly, more comprehen sive spectral differentiation techniques based on an expanded s pectral coverage are required to mitigate these situations and improve the performance of current detection systems [5]. Recently, these efforts have led researchers to look for ins ights in an area where similar issues have been extensively s tudied, namely the remote sensing of vegetation. Since plants r epresents a fundamental resource for all human and animal li fe on the planet, many spectral differentiation technologies have been proposed in this area, including more than 150 vege tation indices [6]. These indices are formed from combinations of s pectral responses of plants to yield a single value that indi cates their vigour and allows their differentiation from su rro nding materials such as water and bare soil. Arguably th e best known and widely employed vegetation index is the NDV I (Normalized Difference Vegetation Index) [6]. It is based o n the characteristic patterns of vegetation reflectance in visib le and near-infrared regions of the light spectrum, being co mputed as (ρNIR − ρR)/(ρNIR + ρR), whereρR andρNIR correspond to the mean reflectance in the red (600-700 nm) and near-infrared (700-1100nm) bands of interest, respectively [7]. In 2008, Nunez and Mendenhall [8] proposed an index for the de t ction of skin signatures inspired in the NDV I. This index, termed Normalized Difference Skin Index and hencefo rth referred to asNDSI8, employs reflectance ( ρ) values captured