Hands, face, space, fresh air

Nicky Steadman discusses combatting coronavirus in practice

Face dissimilarity judgements are predicted by representational distance in deep neural networks and principal-component face space

Despite the importance of face perception in human and computer vision, no quantitative model of perceived face dissimilarity exists. We designed an efficient behavioural task to collect dissimilarity and same/different identity judgements for 232 pairs of realistic faces that densely sampled geometric relationships in a face space derived from principal components of 3D shape and texture (Basel Face Model, BFM). In a comparison of 15 models, we found that representational distances in deep neural networks (DNNs) and Euclidean distances within BFM space predicted human judgements best. A face-trained DNN explained unique variance over simpler models and was statistically indistinguishable from the noise ceiling. Sigmoidal transformation of distances improved performance for all models. Identity judgements were better predicted by Euclidean than angular or radial distances in BFM space. DNNs provide the best available image-computable models of perceived face dissimilarity. The success of BFM space suggests that human face perception is attuned to the natural distribution of faces.

Familiar faces as islands of expertise

Most people recognise and match pictures of familiar faces effortlessly, while struggling to match unfamiliar face images. This has led to the suggestion that true human expertise for faces applies only to familiar faces. This paper extends that idea to the notion that we have isolated ‘islands’ of expertise surrounding each familiar face that allow us to perform better with faces that resemble those we already know. The idea is tested in three experiments. The first shows that familiarity with a person facilitates identification of their relatives. The second shows that people are better able to remember faces that resemble someone they already know. The third shows that while prompting participants to think about resemblance at study produces a large effect on subsequent recognition, there is still a significant effect if there is no such prompt. Face-Space-R is used to illustrate a possible computational explanation of the processes involved.

FACE RECOGNITION SYSTEM BY IMAGE PROCESSING

A wide variety of systems require reliable person recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that only a legitimate user and no one else access the rendered services. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. Face can be used as Biometrics for person verification. Face is a complex multidimensional structure and needs a good computing techniques for recognition. We treats face recognition as a two-dimensional recognition problem. A well-known technique of Principal Component Analysis (PCA) is used for face recognition. Face images are projected onto a face space that encodes best variation among known face images. The face space is defined by Eigen face which are eigenvectors of the set of faces, which may not correspond to general facial features such as eyes, nose, lips. The system performs by projecting pre extracted face image onto a set of face space that represent significant variations among known face images. The variable reducing theory of PCA accounts for the smaller face space than the training set of face. A Multire solution features based pattern recognition system used for face recognition based on the combination of Radon and wavelet transforms. As the Radon transform is in-variant to rotation and a Wavelet Transform provides the multiple resolution. This technique is robust for face recognition. The technique computes Radon projections in different orientations and captures the directional features of face images. Further, the wavelet transform applied on Radon space provides multire solution features of the facial images. Being the line integral, Radon transform improves the low-frequency components that are useful in face recognition