8.5: The behavioral phenomenon: Unfamiliar face

We effortlessly recognize faces of different people, but this ability is significantly different from familiar faces, which belong to our personal acquaintances, and unfamiliar faces (Bindemann, Avetisyan, & Rakow, 2012; Bruce et al., 2001; Hancock et al., 2000; Jenkins, & Burton, 2011; Johnston, & Edmonds, 2009). Although recognizing familiar faces is strikingly stable and reliable performance regardless of poor visual conditions (e.g., poor illumination, low-quality images, and variable viewpoints), recognition of unfamiliar faces appears remarkably poor, even without any poor viewing conditions (Bindemann et al., 2012; Bruce et al., 2001; Hancock et al., 2000). Behavioral (e.g., Bindemann, & Sandford, 2011) and psychophysical evidence (e.g., Haxby et al., 2001) have been supported this phenomenon.

Face-space model

The face-space model also reviewed in 3.1.2, but here focus on face-space model accounting for unfamiliar face. This well-known model pursues to reproduce human’s conceptual representations of faces. One way of the underlying dimensions is multi-dimensional scaling (MDS) in terms of similarity inspired by exemplar models (Busey, 1998). According to Busey (1998), for example, he created six identifiable dimensions (e.g., age, facial hair, and hair color) in order to replicate human face recognition performance by using a bald man image set. When target faces are not bald, the important dimension would be hair color and style (Hancock et al., 2000).

In addition, a statistical analyzing on a face set like principal component analysis (PCA) is a commonly used method in computational recognition task (Hancock et al., 2000). This approach represents a set of faces as a small number of global eigenvectors, which encode the major variations in the input set (Grudin, 2000). However, one serious drawback of PCA is the limitation when there is large within-class variance in the image sets (Kalocsai et al., 1998). Thus, it is likely that efficient information would be neglected because there are several possible views to recognize each face in the dataset (Hancock et al., 2000). Although PCA might provide the dimensionality of the space, not all dimensions are labeled and interpreted (Hancock et al., 2000). According to Dailey et al (1999), Compared to a model using PCA, MDS data by particularly using a kernel density estimation model is more predictable in human face recognition. Although there are efforts to apply the process of human face recognition to computational models and they have become to perform face recognition, it is hard to achieve to reproduce it completely resemble to human perception (Hancock et al., 2000).