Assessing facial approximation accuracy: How do resemblance ratings of disparate faces compare to recognition tests?

In the past, the accuracy of facial approximations has been assessed by resemblance ratings (i.e., the comparison of a facial approximation directly to a target individual) and recognition tests (e.g., the comparison of a facial approximation to a photo array of faces including foils and a target individual). Recently, several research studies have indicated that recognition tests hold major strengths in contrast to resemblance ratings. However, resemblance ratings remain popularly employed and/or are given weighting when judging facial approximations, thus indicating that no consensus has been reached. This study aims to further investigate the matter by comparing the results of resemblance ratings and recognition tests for two facial approximations which clearly differed in their morphological appearance. One facial approximation was constructed by an experienced practitioner privy to the appearance of the target individual (practitioner had direct access to an antemortem frontal photograph during face construction), while the other facial approximation was constructed by a novice under blind conditions. Both facial approximations, whilst clearly morphologically different, were given similar resemblance scores even though recognition test results produced vastly different results. One facial approximation was correctly recognized almost without exception while the other was not correctly recognized above chance rates. These results suggest that resemblance ratings are insensitive measures of the accuracy of facial approximations and lend further weight to the use of recognition tests in facial approximation assessment. (c) 2006 Elsevier Ireland Ltd. All rights reserved.

Computational methods for the detection of facial palsy

We are developing a telemedicine application which offers automated diagnosis of facial (Bell's) palsy through a Web service. We used a test data set of 43 images of facial palsy patients and 44 normal people to develop the automatic recognition algorithm. Three different image pre-processing methods were used. Machine learning techniques (support vector machine, SVM) were used to examine the difference between the two halves of the face. If there was a sufficient difference, then the SVM recognized facial palsy. Otherwise, if the halves were roughly symmetrical, the SVM classified the image as normal. It was found that the facial palsy images had a greater Hamming Distance than the normal images, indicating greater asymmetry. The median distance in the normal group was 331 (interquartile range 277-435) and the median distance in the facial palsy group was 509 (interquartile range 334-703). This difference was significant (P

Similarity in recognition processes used for bodies and faces: Bodies show caricature effects

Because faces and bodies share some abstract perceptual features, we hypothesised that similar recognition processes might be used for both. We investigated whether similar caricature effects to those found in facial identity and expression recognition could be found in the recognition of individual bodies and socially meaningful body positions. Participants were trained to name four body positions (anger, fear, disgust, sadness) and four individuals (in a neutral position). We then tested their recognition of extremely caricatured, moderately caricatured, anticaricatured, and undistorted images of each stimulus. Consistent with caricature effects found in face recognition, moderately caricatured representations of individuals' bodies were recognised more accurately than undistorted and extremely caricatured representations. No significant difference was found between participants' recognition of extremely caricatured, moderately caricatured, or undistorted body position line-drawings. AU anti-caricatured representations were named significandy less accurately than the veridical stimuli. Similar mental representations may be used for both bodies and faces.

Face recognition robust to head pose from one sample image

Most face recognition systems only work well under quite constrained environments. In particular, the illumination conditions, facial expressions and head pose must be tightly controlled for good recognition performance. In 2004, we proposed a new face recognition algorithm, Adaptive Principal Component Analysis (APCA) [4], which performs well against both lighting variation and expression change. But like other eigenface-derived face recognition algorithms, APCA only performs well with frontal face images. The work presented in this paper is an extension of our previous work to also accommodate variations in head pose. Following the approach of Cootes et al, we develop a face model and a rotation model which can be used to interpret facial features and synthesize realistic frontal face images when given a single novel face image. We use a Viola-Jones based face detector to detect the face in real-time and thus solve the initialization problem for our Active Appearance Model search. Experiments show that our approach can achieve good recognition rates on face images across a wide range of head poses. Indeed recognition rates are improved by up to a factor of 5 compared to standard PCA.