Nahumi Nugrahaningsih

Familiarity is not notoriety: phenomenological accounts of face recognition

From a phenomenological perspective, faces are perceived differently from objects as their perception always involves the possibility of a relational engagement (Bredlau, 2011). This is especially true for familiar faces, i.e., faces of people with a history of real relational engagements. Similarly, valence of emotional expressions assumes a key role, as they define the sense and direction of this engagement. Following these premises, the aim of the present study is to demonstrate that face recognition is facilitated by at least two variables, familiarity and emotional expression, and that perception of familiar faces is not influenced by orientation. In order to verify this hypothesis, we implemented a 3 × 3 × 2 factorial design, showing 17 healthy subjects three type of faces (unfamiliar, personally familiar, famous) characterized by three different emotional expressions (happy, hungry/sad, neutral) and in two different orientation (upright vs. inverted). We showed every subject a total of 180 faces with the instructions to give a familiarity judgment. Reaction times (RTs) were recorded and we found that the recognition of a face is facilitated by personal familiarity and emotional expression, and that this process is otherwise independent from a cognitive elaboration of stimuli and remains stable despite orientation. These results highlight the need to make a distinction between famous and personally familiar faces when studying face perception and to consider its historical aspects from a phenomenological point of view.

Protein Motif Retrieval by Secondary Structure Element Geometry and Biological Features Saliency

This paper presents an approach to detect the presence of a given motif in proteins or in protein data bank (PDB). The approach is based on the secondary structure elements (SSEs) geometrical arrangement in 3D space. A motif is represented as a set of SSEs in their specific positions related to a local reference system (LRS). We propose, exploiting the SSE biological feature saliency in the motif LRS construction stage, a planning strategy to speed-up the motif retrieval process. The experimentation has been carried out on a set of 20 proteins selected from the PDB. In detail we tested five different cases: (i) performances on searching a motif within single proteins, (ii) searching motifs on a set of proteins belonging to the same biological family, (iii) searching into single symmetric proteins, (iv) searching on a set of symmetric proteins from the same family, and finally (v) a general motif retrieval from the entire protein dataset. The experimental results showed good motif recognition performances on each test category, and, by exploiting the basic biological features saliency in motif construction, comparing to a previous approach of SSEs block geometrical retrieval based on the Generalized Hough Transform, it was revealed a significant decrease of the time/space computational complexity. It is worth to point out that the computation time for the case of motif absence is significantly lower than the case of motif present.

Pupil Size as a Biometric Trait

We investigate the possibility of using pupil size as a discriminating feature for eye-based soft biometrics. In experiments carried out in different sessions in two consecutive years, 25 subjects were asked to simply watch the center of a plus sign displayed in the middle of a blank screen. Four primary attributes were exploited, namely left and right pupil sizes and ratio and difference of left and right pupil sizes. Fifteen descriptive statistics were used for each primary attribute, plus two further measures, which produced a total of 62 features. Bayes, Neural Network, Support Vector Machine and Random Forest classifiers were employed to analyze both all the features and selected subsets. The Identification task showed higher classification accuracies (0.6194 ÷ 0.7187) with the selected features, while the Verification task exhibited almost comparable performances (~ 0.97) in the two cases for accuracy, and an increase in sensitivity and a decrease in specificity with the selected features.

Biometric Authentication to Access Controlled Areas Through Eye Tracking

Abstract In this chapter we present a survey of biometric methods that exploit eye tracking technology for identification and verification purposes. Thanks to the availability of cheap and portable devices, it is now possible to deploy an eye tracker in several settings, both in indoor and outdoor environments. Unlike traditional techniques that prevent unauthorized access to secured systems and places, eye-based approaches have the advantage of allowing a contactless interaction that can sometimes even occur covertly. Different eye features will be considered, such as fixation data, scanpath characteristics, saccade dynamics, pupil size, and oculomotor traits. Moreover, although not strictly biometric techniques, ATM-like approaches will be also presented, where PINs or passwords are entered using the gaze instead of an ordinary keyboard.

A Hand Gesture Approach to Biometrics

In this paper we present a biometric technique based on hand gestures. By means of the Microsoft Kinect sensor, the user’s hand is tracked while following a circle moving on the screen. Both 3D data about the position of the hand and 2D data about the position of the screen pointer are provided to different classifiers (SVM, Naive Bayes, Classification Tree, KNN, Random Forest and Neural Networks). Experiments carried out with 20 testers have demonstrated that the method is very promising for both identification and verification (with success rates above 90%), and can be a viable biometric solution, especially for soft biometric applications.

Gaze-based biometrics: An introduction to forensic applications

Abstract Eye tracking has for decades been a powerful tool of inspection for scientists and engineers. Thanks to the development of cheap and compact hardware devices, eye tracking applications are being adopted in many fields, from military to marketing. In recent years, gaze-based biometrics has proven to be a noteworthy alternative technology for authentication, surveillance and so on, although there are still efficiency and privacy issues to be addressed. An emerging application of gaze-based biometrics is in the forensic field. This paper provides an introduction and an extended bibliography to such applications, and briefly presents some relevant case studies about the so-called weapon focus effect, identification in a criminal lineup, and lie detection using eye tracking.