A comparison of facial landmark detection methods

Sandikci, Esra Nur; Erdem, Çiğdem Eroğlu; Ulukaya, Sezer

doi:10.1109/siu.2018.8404357

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…In the beginning of 2010s, different methods were proposed in order to perform face alignment procedures and it was shown in the studies that these methods showed high performance [371,397,284]. There are survey papers that summarize the studies on facial landmarking [42,283,163,295,60,348,366,31]. Wu and Ji have classified facial landmark detection methods into three categories as holistic methods, Constrained Local Model (CLM) methods, and regression-based methods [366].…”

Section: Facial Landmarkingmentioning

confidence: 99%

Face recognition: Past, present and future (a review)

Taşkıran

Kahraman

Erdem

2020

Digital Signal Processing

157

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Section: Facial Landmarkingmentioning

confidence: 99%

Face recognition: Past, present and future (a review)

Taşkıran

Kahraman

Erdem

2020

Digital Signal Processing

157

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“…Facial landmarks are detected both in order to perform 2-D face alignment and extract statistical dynamic features during smile activity. Recently, many methods have been proposed in the literature for facial landmark detection [51][52][53][54][55][56]. The treestructure based model proposed by Zhu and Ramanan in 2012 [55] has achieved good landmark localization results on faces in the wild database.…”

Section: Facial Landmark Detectionmentioning

confidence: 99%

Hybrid face recognition under adverse conditions using appearance‐based and dynamic features of smile expression

2020

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Although recent deep‐learning‐based face recognition methods give remarkable accuracies on large databases, their performance has been shown to degrade under adverse conditions (e.g. severe illumination and contrast variations; blur and noise). Under such conditions, soft‐biometric features such as facial dynamics are expected to increase the performance if they are used together with appearance‐based features. We propose a novel hybrid face recognition, which uses appearance‐based features extracted using deep convolutional networks and statistical facial dynamics features extracted from facial landmark positions during smile expression. We evaluated the performances of three different state‐of‐the‐art pre‐trained deep convolutional neural networks (DCNNs) under a variety of severe image distortions with different parameters. The experimental results show that, although the face recognition performance using only DCNN‐based features drops significantly under adverse conditions, the utilization of facial dynamics features together with DCNN‐based features can compensate for the performance loss and increase the accuracy significantly. We believe the proposed system can be useful when face recognition is performed using videos obtained from systems, which may contain blurry and noisy images with a wide range of illumination variations.

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