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The purpose of this paper is to examine how abusive supervision affects employee creativity, incorporating departmental identification as a mediator and face as a moderator based on social identity theory.

The study sample consisted of 207 full-time employees in China.

Results indicate that abusive supervision is negatively related to employee creativity and this relationship is fully mediated by departmental identification. In addition, face held by employees plays a moderating role: the direct effect of abusive supervision on departmental identification and the mediating effect of departmental identification are stronger when face is low rather than high.

This study also has major practical implications for organizations. First, the organizations should fully understand the harmfulness of abusive supervision and try to avoid abusive behaviors. Second, the organizations could enhance employee creativity by promoting departmental identification. Third, employees who hold low face should get more attention and support.

This study makes several theoretical implications. First, findings contribute to enriching one’s understanding of the relationship between abusive supervision and employee creativity in China. Second, the domain of abusive supervision is expanded by empirically testing departmental identification as a crucial psychological mechanism explaining the abusive supervision – employee creativity relationship. Third, this study also advances one’s understanding of social identity process by examining empirically moderating effects of cultural value in the relationship between abusive supervision and employee creativity.

The purpose of this paper is to focus on ecological consumption and test the effect of social value orientation on ecological consumption. What is more, this paper explores how Chinese consumers choose between prosocial and non-prosocial products under the influence of Chinese face culture.

The authors assume that social value orientation will change individual’s ecological consumption through the in-group identification, and simultaneously predict that the influence in pro-self and pro-social consumer groups will vary. Furthermore, Chinese face consciousness will moderate the relationship between ecological consumption and social value orientation. Online research and intercept survey are employed to collect data. In total, 600 questionnaires were distributed.

The results indicate that pro-social individuals prefer sustainable consumption than pro-self-individuals, and in-group identification mediates the effect of social value orientation on ecological consumption. Interestingly, pro-self-individuals’ behaviors have changed dramatically by the influence of face consciousness.

The authors discovered that social value orientation has a deep impact on ecological consumption through in-group identification. The authors tested and verified the dominance of Chinese face culture. Besides, four key elements of China’s “face” construct are proposed, namely, holism, synergy, synchronicity and dynamics, which enlarge the horizon of the theory of face.

This research project focuses on developing techniques and technologies for automatically identifying human faces from images in the situations where face sample collections in the database as well as in the input query images are “as is”, i.e. no standard data collection environment is available. The developed method can also be used in other biometric applications.

The specific method presented in this paper is called scale independent identification (SII). SII allows direct “comparison” between two images in terms of whether the two objects (e.g. faces) in the two images are the same object (i.e. the same individual). SII is developed by extensively using the matrix computation theory and in particular, the singular value decomposition theory.

It is found that almost all the existing methods in the literature or technologies in the market require that a normalization in scale be done before any identification processing. However, it is also found that normalization in scale not only adds additional processing complexity, but also may reduce the identification accuracy. In addition, it is difficult to anticipate an “optimal” scale in advance. The developed SII complements the existing methods in all these aspects.

The only limitation which is also the limitation for many other biometric identification methods is that each object (e.g. individual in human face identification) must have a sufficient number of training samples collected before the method works well.

SII is particularly suitable in law enforcement and/or intelligence applications in which it is difficult or impossible to collect data in a standard, “clean” environment.

The SII method is new, and the paper should be interesting to researchers or engineers in this area, and should also be interesting to companies developing any biometrics‐based identification technologies as well as government agencies.

The introduction of a three-quarter-view database in the PRO-fit facial-composite system has enabled an investigation into the effects of image view in face construction. The purpose of this paper is to examine the impact of constructing full-face and three-quarter-view composites under different encoding conditions. It also examines the potential value of three-quarter-view composites that can be generated automatically from a front-view composite. The authors also investigate whether there is an identification benefit for presenting full-face and three-quarter composites together.

Three experiments examine the impact of encoding conditions on composite construction and presentation of composites at the evaluation stage.

The work revealed that while standard full-face composites perform well when all views of the face have been encoded, care should be taken when a person has only seen one view. When a witness has seen a side view of a suspect, a three-quarter-view composite should be constructed. Also, it would be beneficial for a witness to construct two composites of a suspect, one in full-face view and one in a three-quarter-view, particularly when the witness has only encoded one view. No benefit emerged for use of three-quarter-view composites generated automatically.

This is the first study to examine viewpoint in facial composite construction. While a great deal of research has examined viewpoint dependency in face recognition tasks, composite construction is a reconstruction task involving both recall and recognition. The results indicate that there is a viewpoint effect that is similar to that described in the recognition literature. However, more research is needed in this area.

The practical implications of this research are that it is extremely important for facial composite operators in the field (police operators) to know who will make a good likeness of the target. Research such as this which examines real-life issues is incredibly important. This research shows that if a witness has seen all views of a perpetrator’s face then standard composite construction using a full-face view will work well. However, if they have only seen a single view then it will not.

There are obvious wider societal implications for any research which deals with eyewitness memory and the potential identification of perpetrators.

No research to date has formally examined the impact of viewpoint in facial-composite construction.

Aims to develop an efficient and robust system for real‐time personal identification by automatic face recognition.

A wavelet‐based image hierarchy and a guided coarse‐to‐fine search scheme are introduced to improve the computation efficiency in the face detection task. In addition, a Gabor‐based low feature dimensional pattern is proposed to deal with the face recognition problem.

The proposal of a wavelet‐based image hierarchy and a guided coarse‐to‐fine search scheme is effective to improve the computation efficiency in the face detection task. The introduction of a low feature dimensional pattern is powerful to cope with the transformed appearance‐based face recognition problem. In addition, the use of aggregated Gabor filter responses to represent face images provides a better solution to face feature extraction.

Provides guidance in the design of automatic face recognition system for real‐time personal identification.

Biometrics recognition has been emerging as a new and effective identification technology that attains certain level of maturity. Among many body characteristics that have been used, face is one of the most commonly used characteristics and has drawn considerably large attentions. An automated system to confirm an individual's identity employing features of face is very attractive in many specialized fields.

Introduces a wavelet‐based image hierarchy and a guided coarse‐to‐fine search scheme to improve the computation efficiency in the face detection task. Introduces a Gabor‐based low feature dimensional pattern to deal with the face recognition problem.

The other-race effect shows that people are better recognizing faces from their own-race compared to other-race faces. This effect can have dramatic consequences in applied scenarios whereby face identification is paramount, such as eyewitness identification. This paper aims to investigate whether observers have insights into their ability to recognize other-race faces.

Chinese ethnic observers performed objective measures of own- and other-race face recognition – the Cambridge Face Memory Test Chinese and the Cambridge Face Memory Test original; the PI20 – a 20-items self-reported measured of general face recognition abilities; and the ORE20 – a new developed 20-items self-reported measure of other-race face recognition.

Recognition of own-race faces was better compared to other-race faces. This effect was also evident at a phenomenological level, as observers reported to be worse recognizing other-race faces compared to own-race faces. Additionally, although a moderate correlation was found between own-race face recognition abilities and the PI20, individual differences in the recognition of other-race faces was only poorly associated with observers’ scores in the ORE20.

These results suggest that observers’ insights to recognize faces are more consistent and reliable for own-race faces.

Self-reported measures of other-race recognition could produce misleading results. Thus, when evaluating eyewitness’ accuracy identifying other-race faces, objective measures should be used.

In contrast to own race recognition, people have very limited insights into their recognition abilities for other race faces.

To study the mathematical image coding approaches used in two types of biometric systems, and the physical nature of those biometrics.

Gives details of algorithms used to encode data from images in established and new automatic iris recognition systems. Then examines face recognition techniques based on geometry, texture and three‐dimensional data.

Most commercial iris recognition systems are based on the algorithms developed by one man, John Daugman. Whilst iris systems can be used to check a person's identity against a large database of enrolled people, face recognition systems are currently only capable of use in one‐to‐one recognition mode, or in identification mode against a very small database. The iris is very distinctive and stable over time, but the face is much more variable and therefore difficult to identify with accuracy.

Provides the general scientific reader with some insight into the specialised field of biometric recognition.

Biometric systems are widely used for face recognition. They have rapidly developed in recent years. Compared with other approaches, such as fingerprint recognition, handwriting verification and retinal and iris scanning, face recognition is more straightforward, user friendly and extensively used. The aforementioned approaches, including face recognition, are vulnerable to malicious attacks by impostors; in such cases, face liveness detection comes in handy to ensure both accuracy and robustness. Liveness is an important feature that reflects physiological signs and differentiates artificial from real biometric traits. This paper aims to provide a simple path for the future development of more robust and accurate liveness detection approaches.

This paper discusses about introduction to the face biometric system, liveness detection in face recognition system and comparisons between the different discussed works of existing measures.

This paper presents an overview, comparison and discussion of proposed face liveness detection methods to provide a reference for the future development of more robust and accurate liveness detection approaches.

The European directive on the prevention of money laundering was adopted on 10th June, 1991, and has been in force from 1st January, 1993 (even if not all the Member States have implemented it within the scheduled deadline and there is still an infringement case before the European Court of Justice in Luxembourg).

The objective of this paper is to perform infrared (IR) face recognition efficiently with convolutional neural networks (CNNs). The proposed model in this paper has several advantages such as the automatic feature extraction using convolutional and pooling layers and the ability to distinguish between faces without visual details.

A model which comprises five convolutional layers in addition to five max-pooling layers is introduced for the recognition of IR faces.

The experimental results and analysis reveal high recognition rates of IR faces with the proposed model.

A designed CNN model is presented for IR face recognition. Both the feature extraction and classification tasks are incorporated into this model. The problems of low contrast and absence of details in IR images are overcome with the proposed model. The recognition accuracy reaches 100% in experiments on the Terravic Facial IR Database (TFIRDB).