Ritos y Rituales (Costumbres y Comportamientos)

In order to exhibit the generic nature and applicability of the framework

presented in Section 5.2, in the current Section the state of the art 3D face

recognition system presented by Berretti et al. in [251] is herein utilized, as the baseline algorithm.

The proposed approach has been evaluated on the BIOTAFTOTITA 3D

Face Dataset (DB.F.1) (see Section 3.2.2), that contains facial recordings of

54 subjects under different scenarios. Although the utilized 3Dface match-

ing algorithm exhibits high robustness in difficult environmental conditions and strange poses, within the current work, frames form the simple case of

neutral pose in 0o have been selected pose has selected for both gallery and

probe.

In particular, only the first 5 most discriminative frames of each person have been selected to form the gallery signatures. The distinctiveness is

Figure 7.11.: Scores Distribution before and after the application of the pro- posed framework (Gaussian Clusters) in the HUMABIO RIT- Time experiment.

Figure 7.12.: Scores Distribution before and after the application of the pro- posed framework (Gaussian Clusters) in the ACTIBIO RIT- Time experiment.

evaluated by creating a confusion matrix with the similarity measure, de- scribed below, between all frames of the recorded (gallery) session. This way, 5 frames of this session are selected to be included in the biometric sig-

nature, while another session regarding a neutral pose in 0o for each subject

has been used only for testing.

Similarly to the approach followed in Section 7.1.1, the proposed proba- bilistic framework is applied, while the performance of the combined system is evaluated and compared to similar state-of-the-art augmenting frame- works, that use soft biometric traits.

In order to remove the noisy information from the facial images, such as areas with hairs or background areas, the work of Beretti et al. has been enriched with a preprocessing step for drawing face-specific ellipses (first

row in Figure 7.14). In particular, by using as the center of the ellipse the exact location of the nosetip, the axes of the ellipse are calculated as a function of the distance between the eyes [339].

The step for the detection of the nose tip precedes the background seg- mentation and is initially based on an initial detection of the location of

the nose tip (N_tkinect) from the coloured image, as delivered by the Kinect

SDK toolkit. However, since the detection accuracy of this algorithm is not sufficient (red spots in Figure 3) a post processing algorithm has been

initiated. In particular, all points within a sphere of 4cm around N_tkinect

undergo PCA transformation and the new nose tip location is calculated as the median value of the closest points to the origin of the depth axis. Then, by mapping this depth value on the initial surface, one can easily es-

timate a good approximation of the actual nose tip location (Nt(x0, y0, z0)),

as indicated by the blue spots in Figure 7.13.

Figure 7.13.: The red spots indicate the location of the nose tip as detected by processing the colour related information of the face, while the blue ones point the location estimated by processing the depth information.

Then, the Dijkstra algorithm is applied on all facial points within the ellipse, all geodesic distances are normalized to the eyes-to-nose distance

and this way, isogeodesic stripes of equal width (i.e. 1cm) can be esti-

mated, concentric and centered on the nose tip (see third row in Figure

7.14). Thereafter, the so-called 3D weighted walkthroughs (3DW W s) are

computed between pairs of isogeodesic stripes (interstripe 3DW W) and be-

tween each stripe and itself (intrastripe 3DW W), as described in [251]. In

particular, the 3DW W s are computed aggregate measures (i.e. directional

the set of points of two spatial entities (i.e. isogeodesic stripes). Finally,

the computed 3DW W sare cast to a graph representation where stripes are

used to label the graph nodes and 3DW W s to label the graph edges.

Figure 7.14.: First Row: Face-specific ellipses are drawn on the facial images so as to exclude any noisy, non-facial. points from processing;

Second Row: 3D reconstruction of the extracted facial image;

Third Row: The extracted isogeodesic stripes are drawn within the boundaries defined by each face-specific ellipse.

This way, the face recognition problem is reduced to an efficient graph matching issue that is suited to being employed in very large data sets with the support of appropriate index structures. Thus, the measure of

the similarity p(ω|xc) between two face models represented through their

corresponding graphs is a combination of both the intrastripe and interstripe 3DW W ssimilarity measure, and the second summation.

All geodesic distances are normalized with respect to the Euclidean eyes- to-nose distance in [251], so as to maintain a common reference for all com- parisons. This way, significant information regarding the actual size of the face is discarded in favour of efficiency in processing. However, providing the distances between the so-called “nodal points” (i.e. eyes, nose and mouth), and the structural proportions of the face of the average user, the afore- mentioned lost information can be retrieved and significantly contribute to

eyes-to-nose xs2 and c) nose-to-mouth xs3 distances for the set X of the

available soft biometrics X={xs1, xs2, xs3}.

In the current work the detection of the these nodal points is performed via the utilization of the Face Tracking algorithm included in the “Kinect for Windows SDK” that is able to locate and track the movement and the orientation of the face and all its corresponding nodal points (i.e. eyes, eyebrows, mouth) with sufficient accuracy. However, small fluctuations in the distances between the measured aforementioned points are very likely to occur. This can be easily interpreted as the systematic error introduced in each measurement. Similarly to the analysis performed in Section 7.1.1, the independence between the errors of the utilized soft biometric traits can be easily proven by a similar analysis with the one presented in Section 7.1.1.

Based on the methodology described in Section 5.2 and as it was proven by the analysis presented in Section 7.1.1, the Gaussian Clustering performs better than the rest. This way, provided that the ISODATA algorithm indi- cated a number of 13 clusters, the soft biometric feature space is described as

a Gaussian mixture (Figure 7.15) via the expectation-maximization (EM)

algorithm.

Figure 7.15.: The 3Dsoft biometric feature space has been partitioned in 13

Gaussian Clusters, according to the spatial proximity of the features.

Thereby each 3DGaussian is described as N(fω|µk,Σk) = 1 (2π)Z/2_|Σ k|1/2 e−12(fω−µk) T_Σ−1 k (fω−µk) _(7.1)

whereµkandΣkare the 3Dmean vector and the and 3×3 covariance matrix

of the kth Gaussian, respectively, and Vector fω includes all utilized soft

biometric trait values,fω ={xsn,1(ω), . . . , xsn,Z(ω)}. At the authentication

stage, the assignment of a user to a cluster is performed via the maximum

likelihood (M L) criterion.

In the same context, the modelling of the noise induced for each utilized soft biometric feature can be seen in Figure 7.16.

Figure 7.16.: Distribution of the induced noise during in (a) Eye-to-Eye distance (b) Nose-to-Eye distance (c) Nose-to-Mouth distance and the corresponding fitting curves.

Experimental results of the proposed approach

Concerning the authentication and identification performance of the

proposed approach, the Receiver Operating Characteristics (ROC) curves and the corresponding Cumulative Matching Scores (CMS) curves are illus- trated in Figure 7.17(a) and Figure 7.17(b), respectively.

The reader can easily notice a significant fall in the equal error rate EER

point from 6% to 1.9% in the demanding BIOTAFTOTITA database, after

the application of the proposed algorithm, while the approach proposed by

Moustakas et al. [6] and Marcialis et al. [220] achieves only a 4.8% of EER.

Similar improvements are concluded by the experimental results regarding the identification performance of the system, where the proposed algorithm converges to 100% of correct identifications at Rank-2, while the approach suggested in citeMoustakas10 and in [220] starts with a lower identification rate (i.e. 89% at Rank-1) and converges to 100% only at Rank-3.

Figure 7.17.: (a) Cumulative Matching Scores (CM S) and (b) Receiver Op-

erating Characteristics (ROC) for the 3DFace Recognition al-

gorithm with and without counting in the contribution of the Soft Biometric Traits.

As it can be seen in Figure 7.18(a), the genuine scores are completely

mixed with the ones of the impostors. However, once applying the proposed enhanced matching probability (Equation (5.18)), a significant separation of them can be noticed in Figures 7.18(b).

Figure 7.18.: Scores Distribution with and without counting in the contri- bution of the Soft Biometric Traits.