Understanding factors underlying actual consumption of organic food: The moderating effect of future orientation

The majority of past studies focused on investigating the motivational factors to purchase organic food as a proxy to foster organic food consumption. However, the preceding studies’ foci do not embrace the consumption itself where purchasing may come secondary to consumption decisions. Consumption reflects high involvement with the product; and the barriers and motivations are as real as the product itself, which makes it an ideal moment to examine the motivation. The research model was analyzed using the Partial Least Square Structural Equation Modeling technique. Results show that product-specific attitude (PSA), willingness to pay (WTP) and perceived availability (PA) had a significant positive influence on individuals’ organic food consumption (OFC), while environmental attitude (EA) and subjective norms (SN) were not significantly related. The moderating role of future orientation (FO) between PSA, EA, WTP and OFC were examined and found to be significant except for EA. The result suggests that PSA and WTP are stronger and higher respectively when future orientation is high. The research provides a significant insight and better understanding of organic food actual consumption behavior and adds a new momentum to the growing literature. Discussions and implications of these findings are further discussed

Examining green consumerism motivational drivers: does premium price and demographics matter to green purchasing?

Environmental deterioration caused by consumers' non-sustainable consumption pattern is putting a strain on the environment and is hindering sustainable development. In order to impede this effect and promote a more sustainable economy, one solution is to reduce or shift consumption from conventional products to green products. The unfortunate reality indicates that inadequate information on how to promote consumers' green behavioral intention is slowing the growth of green markets; such inadequacy appears as a prevailing obstacle facing firms while developing segments and communicating strategies for effectively promoting green products. The mentioned impact is more prevailing and most experienced in countries like Malaysia. Hence, consumers' behavioral intentions must be better understood in order to strengthen knowledge about fostering green purchases. This study aims to determine the motivational factors that influence green purchasing intention and simultaneously assess the moderator roles of the premium price and demographic characteristics – given that consumers' degree of greenness varies. A survey was administered and a total of 405 usable questionnaires were obtained. Structural equation modeling (SEM) was applied to test the hypotheses. Results indicate that environmental attitude, eco-label and cultural value (man–nature orientation) significantly influence the green purchase intention. The result also indicates that the premium price has no moderating effect, denying its role as one of the main barriers for consumers to walk their talk as it has previously been reported by studies and opinion polls. In addition, the findings revealed that education level and gender have a significant positive moderation effect. This suggests that green purchase intentions' motivational factors are greater among highly educated individuals especially with female consumers in particular. This study contributes to the understanding of the main factors that motivate consumers' intention to purchase green products in Malaysia. It also offers insights and discusses implementations for manufacturers, marketers and policy makers concerned with the drivers that motivate consumers' green purchasing intentions which require different marketing plan and strategy than conventional products

Fusing Facial Features for Face Recognition

Face recognition is an important biometric method because of its potential applications in many fields, such as access control, surveillance, and human-computer interaction. In this paper, a face recognition system that fuses the outputs of three face recognition systems based on Gabor jets is presented. The first system uses the magnitude, the second uses the phase, and the third uses the phase-weighted magnitude of the jets. The jets are generated from facial landmarks selected using three selection methods. It was found out that fusing the facial features gives better recognition rate than either facial feature used individually regardless of the landmark selection method

The Effect of Database Type on Face Recognition Performance for Surveillance Applications

Face recognition is one of the most important biometric approaches due to its potential applications in surveillance monitoring and access control. This paper presents a PCA and SVM based face recognition system for surveillance application. A proposed training database selection criteria suitable for surveillance application which consist of 1 mean image per distance class from all the available database sessions is also used for the face recognition system. In this study, the ChokePoint database, specifically the grayscale (PPG) and colored (MPCI) versions of the ChokePoint database, were selected for this work. The objectives of this work is to investigate the effect of the using different training data as well as using different similarity matching method on face recognition for surveillance application. It was found that regardless of the type of databases used, the recognition output pattern on different training data selection criteria was found to be similar. It was also found that regardless of the similarity matching method used, the face recognition system also shows the same recognition performance pattern. The experiment suggests that the proposed training database selection criteria will give similar recognition performance regardless of databases type or face recognition technique used. Overall, the ChokePoint colour database (MPCI) gives better recognition performance than the ChokePoint grayscale database (PPG). Finally, it can be concluded that using 1 mean image per class from all the available database sessions (Case-6) is better compared to using 1 image per class that are randomly selected from all the database sessions (Case-4). Even though a straight comparison between this work proposed system and several published system is not meaningful as different face recognition approaches and experiment criteria are used, nevertheless, this work proposed method performs with 100% recall and reject recognition rate

A Comparison of the YCBCR Color Space with Gray Scale for Face Recognition for Surveillance Applications

Face recognition is an important biometric method because of its potential applications in many fields, such as access control and surveillance. In this paper, the performance of the individual channels from the YCBCR colour space on face recognition for surveillance applications is investigated and compared with the performance of the grayscale. In addition, the performance of fusing two or more colour channels is also compared with that of the grayscale. Three cases with a different number of training images per persons were used as a test bed. It was found out that, the grayscale always outperforms the individual channel. However, the fusion of CBxCR with any other channel outperforms the grayscale when three images of the same class from the same database are used for training. Regardless of the cases used, the CBxCR channel always gave the best performance for the individual colour channels. It was found that, in general, increasing the number of fused channels increases the performance of the system. It was also found that the grayscale channel is the better choice for face recognition since it contains better quality edges and visual features which are essential for face recognition

Component-based Face Detection in Colour Images

Abstract: Face detection is an important process in many applications such as face recognition, person identification and tracking, and access control. The technique used for face detection depends on how a face is modelled. In this paper, a face is defined as a skin region and a lips region that meet certain geometrical criteria. Thus, the face detection system has three main components: a skin detection module, a lips detection module, and a face verification module. The Multi-layer perceptron (MLP) neural networks was used for the skin and lips detection modules. In order to test the face detection system, two databases were created. The images in the first database, called In-house, were taken under controlled environment while those in the second database, called WWW, were collected from the World Wide Web and as such have no restriction on lighting, head pose or background. The system achieved a correct detection rate of 87 and 80 percent on the In-house and WWW databases respectivel

Face Recognition State-of-the-art, Enablers, Challenges and Solutions: A Review

In the past decade, face recognition has gained an important role among the most frequently used image processing applications and the availability of viable technologies in this field has also contributed significantly to this. Face recognition has become an enabler in healthcare, surveillance, photo cataloging, attendance, and much more, which will be discussed in this review paper. Despite rapid progress in face-recognition technology, various challenges such as variations, occlusion, facial expressions, aging and many more that affect the performance of the system still need to be addressed. This paper presents a review on the state-of-the-art, enablers, challenges and solutions for face recognition. Face recognition can be categorized into three groups; namely global approach, local feature approach, and hybrid approach. The global approach uses the whole face as input for the face recognition system. The local approach uses measurements between important landmarks of a face and certain face region selection for training. The hybrid approach blends global and local approaches in which the hybrid approach uses the best global and local approach methods. The challenges of face recognition are; (i) automated face detection where difficulties lies on detecting a person's face, (ii) pose variations cause by rotation of people’s head, (iii) face occlusion, (iv) facial expression changes, (v) ageing of the face, (vi) varying illumination conditions, (vii) low image resolution, (viii) identity look-alike or twin, and (ix) other technical difficulties. Finally, the solutions to each of the highlighted challenges were described. The survey found that all the images considered for training and testing were made up of RGB images. With the rapid growth of computer technology in terms of computing speed and the increasingly sophisticated functions of smartphones, multispectral or even hyperspectral imagery could be considered for face-recognition research