Robust face recognition using posterior union model based neural networks

Lin, Jie; Ji, Ming; Crookes, Danny

doi:10.1049/iet-cvi.2008.0043

Cited by 18 publications

(6 citation statements)

References 31 publications

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“…The parameters of the transmission lines are illustrated in Table 1. The model of VSC proposed in [41][42][43][44][45][46][47][48][49][50] has been adopted, vector control, and traditional proportional-integral controller. To obtain the training table of the NN, the system has been simulated…”

Section: Simulationmentioning

confidence: 99%

Artificial intelligence‐based short‐circuit fault identifier for MT‐HVDC systems

Hossam-Eldin

Lotfy

Elgamal³

et al. 2018

IET Generation, Transmission & Distribution

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Section: Simulationmentioning

confidence: 99%

Artificial intelligence‐based short‐circuit fault identifier for MT‐HVDC systems

Hossam-Eldin

Lotfy

Elgamal³

et al. 2018

IET Generation, Transmission & Distribution

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“…The accuracy of facial recognition is usually affected by substantial intra‐class variations due to underlying factors [4], including age, pose [5], lighting [6], and expression [7]. As a result, the majority of the current works focuses on the efforts of minimising the effect of such variations that could deteriorate the overall performance of facial recognition [1].…”

Section: Related Workmentioning

confidence: 99%

Proposed face recognition system after plastic surgery

Ali¹,

Sagayan²,

Malik³

et al. 2016

IET Computer Vision

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Plastic surgery is considered as a challenging research issue in the field of face recognition. Nevertheless, it has yet to be studied from theoretical and experimental perspectives. In this study, the authors proposed a facial recognition system for recognising faces after plastic surgery, which fuses the scores of two feature‐based and texture‐based algorithms. The feature based algorithm is the image GIST global descriptor and the texture‐based algorithm is the local binary pattern (LBP) of silence points. First, the local texture descriptor LBP was applied over a set of key points (silence points) in the face image rather than applying it over the entire face area. This proposed feature set is based on the assumption that only those LBP patterns with certain meaning, such as an edge or corner, will be useful for recognising faces that have undergone plastic surgery. The second set of features was extracted using a global descriptor, which is the GIST descriptor, to obtain a basic and a subordinate level description of the perceptual dimension. The performance of the proposed system surpassed the performance of a number of state‐of‐the‐art face recognition after plastic surgery, with a maximum verification accuracy of more than 91%.

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“…In [24], the PUM was used in a subband approach to noisy speech and speaker recognition where the aim was to base the recognition as much as possible on the cleanest frequency bands without any prior knowledge of the noise in any subband. In [25], the PUM was used to combine separate feature streams from image segments for face recognition where the images were subject to various forms of corruption. As both these problems are analogous to the problem under investigation in this paper and the fact that the PUM performs under the same operating assumptions as MWSP, i.e., no prior knowledge of the noise in any stream is assumed, we chose the PUM as a suitable baseline method for comparison with MWSP.…”

Section: A Posterior Union Modelmentioning

confidence: 99%

Robust Audio-Visual Speech Recognition Under Noisy Audio-Video Conditions

Stewart

Seymour

Pass³

et al. 2014

IEEE Trans. Cybern.

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This paper presents the maximum weighted stream posterior (MWSP) model as a robust and efficient stream integration method for audio-visual speech recognition in environments, where the audio or video streams may be subjected to unknown and time-varying corruption. A significant advantage of MWSP is that it does not require any specific measurements of the signal in either stream to calculate appropriate stream weights during recognition, and as such it is modality-independent. This also means that MWSP complements and can be used alongside many of the other approaches that have been proposed in the literature for this problem. For evaluation we used the large XM2VTS database for speaker-independent audio-visual speech recognition. The extensive tests include both clean and corrupted utterances with corruption added in either/both the video and audio streams using a variety of types (e.g., MPEG-4 video compression) and levels of noise. The experiments show that this approach gives excellent performance in comparison to another well-known dynamic stream weighting approach and also compared to any fixed-weighted integration approach in both clean conditions or when noise is added to either stream. Furthermore, our experiments show that the MWSP approach dynamically selects suitable integration weights on a frame-by-frame basis according to the level of noise in the streams and also according to the naturally fluctuating relative reliability of the modalities even in clean conditions. The MWSP approach is shown to maintain robust recognition performance in all tested conditions, while requiring no prior knowledge about the type or level of noise.

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Robust face recognition using posterior union model based neural networks

Cited by 18 publications

References 31 publications

Artificial intelligence‐based short‐circuit fault identifier for MT‐HVDC systems

Artificial intelligence‐based short‐circuit fault identifier for MT‐HVDC systems

Proposed face recognition system after plastic surgery

Robust Audio-Visual Speech Recognition Under Noisy Audio-Video Conditions

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