Marketing data collection from face images using neural networks

Nakano, Miyoko; Yasukata, Fumiko; Fukumi, Minoru

doi:10.1109/nsip.2005.1502242

Cited by 4 publications

(4 citation statements)

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“…Nakano et al [9] focused on the edge information and exploited a neural network (NN) classifier for gender recognition. In particular, they computed the density histograms of the edge images, which were successively treated as input features for the NN.…”

Section: Introductionmentioning

confidence: 99%

Facial gender recognition using multiple sources of visual information

Matta

Saeed

Mallauran

et al. 2008

2008 IEEE 10th Workshop on Multimedia Signal Processing

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In this article we present a novel multimodal gender recognition system, which successfully integrates the head and mouth motion information with facial appearance by taking advantage of a unified probabilistic framework. In fact, we develop a temporal subsystem that has an extended feature space consisting of parameters related to head and mouth motion; at the same time, we introduce a complementary spatial subsystem based on a probabilistic extension of the eigenface approach. In the end, we implement an integration step to combine the similarity scores of the two parallel subsystems, using a suitable opinion fusion (or score fusion) strategy. The experiments show that not only facial appearance but also head and mouth motion possess a potentially relevant discriminatory power, and that the integration of different sources of biometric information from video sequences is the key strategy to develop more accurate and reliable recognition systems. I. INTRODUCTIONHuman face contains a variety of information for adaptive social interactions amongst people. In fact, individuals are able to process a face in a variety of ways to categorize it by its identity, along with a number of other demographic characteristics, such as gender, ethnicity, and age. In particular, recognizing human gender is important since people respond differently according to gender. In addition, a successful gender classification approach can boost the performance of many other applications, including person recognition and smart human-computer interfaces.In this article, we address the problem of automatic gender recognition by exploiting the physiological and behavioural aspects of the face at the same time. We have already investigated the use of the head and mouth motion information for person recognition in an earlier research study [1]. Currently, comforted by the promising results obtained by this previous approach, we explore the possibility of using head motion, mouth motion and facial appearance in a gender recognition scenario. Hence, we propose a multimodal recognition approach that integrates the temporal and spatial information of the face through a probabilistic framework.The remainder of this article is organised as follows: in section II we propose a short review of related works, and then in section III we detail our recognition system; afterwards we report and comment the experiments in section IV, and finally we conclude this paper with remarks and future work in section V.

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Section: Introductionmentioning

confidence: 99%

Facial gender recognition using multiple sources of visual information

Matta

Saeed

Mallauran

et al. 2008

2008 IEEE 10th Workshop on Multimedia Signal Processing

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“…Face recognition can be used in mobile devices because of their flexibility but it has constraints on processing power, limited storage, limited bandwidth, privacy, and security issues [14][15]. Face recognition method is generally used in the security, username identification, password application, and marketing applications (based on identity) [16][17][18][19][20][21][22]. It is clear that face recognition system is used in application.…”

Section: Literature Reviewmentioning

confidence: 99%

Face recognition based physical layer security system for next-generation wireless communication

Islam¹,

Ahmed²,

Hossain³

et al. 2023

World J. Adv. Res. Rev.

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Security system in every sector all over the world is a most demandable and crucial issue to protect the fake user especially in wireless communication system. The most common feature in current wireless communication system is only transmitted users’ information; consequently, it is difficult to identify the actual sender to attain the desired security. Face recognition is one of the most popular security systems in biometric authentication, which can detect intruders to restricted or high-security areas in wireless communication sector, and help in minimizing the face user. In this paper, we proposed a system that is the senders will transmit message information with their image to confirm the original senders. A face-recognition algorithm will be implemented at the receiver end. When a senders’ transmitted information enters to the receiver section, the image of sender will be separated from original message signal and sent to the face-recognition algorithm to be analysed and compared with an existing database of trusted user. An alarm goes off if the user is not recognized.

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“…al. [7] computed the edge information and exploited a neural network classifier for gender recognition. Lu et.…”

Section: Introductionmentioning

confidence: 99%

Gender Recognition From Face Images With Dyadic Wavelet Transform and Local Binary Pattern

Hussain

Ullah

Aboalsamh

et al. 2013

Int. J. Artif. Intell. Tools

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Gender recognition from facial images plays an important role in biometric applications. Employing Dyadic wavelet Transform (DyWT) and Local Binary Pattern (LBP), we propose a new feature descriptor DyWT-LBP for gender recognition. DyWT is a multi-scale image transformation technique that decomposes an image into a number of sub-bands which separate the features at different scales. DyWT is a kind of translation invariant wavelet transform that has a better potential for detection than Discrete Wavelet Transform (DWT). On the other hand, LBP is a texture descriptor and is known to be the best for representing texture micro-patterns, which play a key role in the discrimination of different objects in an image. For DyWT, we used spline dyadic wavelets (SDW). There exist many types of SDW; we investigated a number of SDWs for finding the best SDW for gender recognition. The dimension of the feature space generated by DyWT-LBP descriptor becomes excessively high. To tackle this problem, we apply a feature subset selection (FSS) technique that not only reduces the number of features significantly but also improves the recognition accuracy. Through a large number of experiments performed on FERET and Multi-PIE databases, we report for DyWT-LBP descriptor the parameter settings, which result in the best accuracy. The proposed system outperforms the stat of the art gender recognition approaches; it achieved a recognition rate of 99.25% and 99.09% on FERET and Multi-PIE databases, respectively.

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Marketing data collection from face images using neural networks

Cited by 4 publications

References 0 publications

Facial gender recognition using multiple sources of visual information

Facial gender recognition using multiple sources of visual information

Face recognition based physical layer security system for next-generation wireless communication

Gender Recognition From Face Images With Dyadic Wavelet Transform and Local Binary Pattern

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