Given the ecological importance of face recognition, it is not surprising that the visual system is capable of processing faces with remarkable efficiency. When presented with a face, information is rapidly extracted to detect and categorize it as a face, followed by face-specific information such as age, gender, and identity. According to cognitive and neural models, the processes underlying face recognition encompass a sequence of steps that begin with a perceptual or visual analysis followed by more image-invariant and identity-selective representations. Importantly, it is only familiar faces for which we have acquired long-term face memories that reach the final stages of identity processing to permit robust, image-invariant behavioural recognition. A key aspect of face processing is that it is fast and automatic. This can be said for both high-level categorization (i.e., detecting that a stimulus is a face) and for encoding at the identity-level. The purpose of these experiments was to use novel electrophysiological and psychophysical techniques to characterize these fast and automatic categorization processes. Experiment 1 and 2 used an implicit visual discrimination paradigm (fast periodic visual stimulation; FPVS) combined with electroencephalography (EEG) to isolate identity-specific neural responses to a personally familiar face, the own-face, and an unfamiliar stranger face. Experiment 1 showed that identity-specific responses recorded over the occipito-temporal region were stronger for a personally familiar face compared to the unfamiliar control identity, while the response to the own-face was even greater than to a personally familiar friend. In Experiment 2, identity-specific responses for a given identity were measured in participants both before and after real-world familiarization. As expected, the results showed a significant increase in the identity-specific response once participants became personally familiar with the test identities. In Experiment 3, we used saccadic eye movements to estimate the lower bounds of the speed of face categorization, and in particular to investigate the question of whether this categorization occurs during early feedforward processing. The results support the view that information needed to detect and selectively respond to face stimuli happens during the earliest visual processing. Collectively, these studies provide additional insight on the mechanisms underlying rapid and automatic face detection and face identity recognition. / Graduate

Yes / Principal component analysis (PCA) is an elegant mechanism that reduces the dimensionality of a dataset to bring out patterns of interest in it. The preprocessing of facial images for efficient face recognition is considered to be one of the epitomes among PCA applications. In this paper, we introduce a novel modification to the method of PCA whereby we propose to utilise the inherent averaging ability of the discrete Biharmonic operator as a preprocessing step. We refer to this mechanism as the BiPCA. Interestingly, by applying the Biharmonic operator to images, we can generate new images of reduced size while keeping the inherent features in them intact. The resulting images of lower dimensionality can significantly reduce the computational complexities while preserving the features of interest. Here, we have chosen the standard face recognition as an example to demonstrate the capacity of our proposed BiPCA method. Experiments were carried out on three publicly available datasets, namely the ORL, Face95 and Face96. The results we have obtained demonstrate that the BiPCA outperforms the traditional PCA. In fact, our experiments do suggest that, when it comes to face recognition, the BiPCA method has at least 25% improvement in the average percentage error rate.

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Yes / This study aimed to develop a clinical test of face perception which is applicable to a wide range of patients and can capture normal variability. The Caledonian face test utilises synthetic faces which combine simplicity with sufficient realism to permit individual identification. Face discrimination thresholds (i.e. minimum difference between faces required for accurate discrimination) were determined in an "odd-one-out" task. The difference between faces was controlled by an adaptive QUEST procedure. A broad range of face discrimination sensitivity was determined from a group (N=52) of young adults (mean 5.75%; SD 1.18; range 3.33-8.84%). The test is fast (3-4min), repeatable (test-re-test r2=0.795) and demonstrates a significant inversion effect. The potential to identify impairments of face discrimination was evaluated by testing LM who reported a lifelong difficulty with face perception. While LM's impairment for two established face tests was close to the criterion for significance (Z-scores of -2.20 and -2.27) for the Caledonian face test, her Z-score was -7.26, implying a more than threefold higher sensitivity. The new face test provides a quantifiable and repeatable assessment of face discrimination ability. The enhanced sensitivity suggests that the Caledonian face test may be capable of detecting more subtle impairments of face perception than available tests. / None

Yes / Faces are highly complex stimuli that contain a host of information. Such complexity poses the following questions: (a) do observers exhibit preferences for specific information? (b) how does sensitivity to individual face parts compare? These questions were addressed by quantifying sensitivity to different face features. Discrimination thresholds were determined for synthetic faces under the following conditions: (i) ‘full face’: all face features visible; (ii) ‘isolated feature’: single feature presented in isolation; (iii) ‘embedded feature’: all features visible, but only one feature modified. Mean threshold elevations for isolated features, relative to full-faces, were 0.84x, 1.08, 2.12, 3.34, 4.07 and 4.47 for head-shape, hairline, nose, mouth, eyes and eyebrows respectively. Hence, when two full faces can be discriminated at threshold, the difference between the eyes is about four times less than what is required when discriminating between isolated eyes. In all cases, sensitivity was higher when features were presented in isolation than when they were embedded within a face context (threshold elevations of 0.94x, 1.74, 2.67, 2.90, 5.94 and 9.94). This reveals a specific pattern of sensitivity to face information. Observers are between two and four times more sensitive to external than internal features. The pattern for internal features (higher sensitivity for the nose, compared to mouth, eyes and eyebrows) is consistent with lower sensitivity for those parts affected by facial dynamics (e.g. facial expressions). That isolated features are easier to discriminate than embedded features supports a holistic face processing mechanism which impedes extraction of information about individual features from full faces.

Matching unfamiliar faces is known to be a very difficult task. Yet, despite this, we frequently rely on this method to verify people's identity in high security situations, such as at the airport. Because of such security implications, recent research has focussed on investigating methods to improve our ability to match unfamiliar faces. This has involved methods for improving the document itself, such that photographic-ID presents a better representation of an individual, or training matchers to be better at the task. However, to date, no method has demonstrated significant improvements that would allow the technique to be put into practice in the real world. The experiments in this thesis therefore further explore methods to improve unfamiliar face matching. In the first two chapters both variability and feedback are examined to determine if these previously used techniques do produce reliable improvements. Results show that variability is only of use when training to learn a specific identity, and feedback only leads to improvements when the task is difficult. In the final chapter, collaboration is explored as a new method for improving unfamiliar face matching in general. Asking two people to perform the task together did produce consistent accuracy improvements, and importantly, also demonstrated individual training benefits. Overall, the results further demonstrate that unfamiliar face matching is difficult, and although finding methods to improve this is not straightforward, collaboration does appear to be successful and worth exploring further. The findings are discussed in relation to previous attempts at improving unfamiliar face matching, and the effect these may have on real world applications.

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