Scotney, Bryan

Wan, Huan

Wang, H.

Wei, X

English

In the past a few years, a number of new loss functions are proposed which considerably acceleratethe development of the deep neural network on face recognition. As a well-known loss, Centre loss cansignificantly improve the face recognition performance compared with using Softmax loss alone. Theinitial idea of the Centre loss is building a penalty by calculating the linear sum of all the Squared Euclidean distances between the within-class samples and the corresponding class centre. However, this isnot a proper option, as the discriminative power of the deep features is determined by the samples on theclass edge instead of the ones close to the class centre. So, a new loss function is proposed in this paper,which is based on the Minkowski distance and the Centre loss for improving the performance on unconstrained face recognition. With this new loss, the impact of the samples on the class edge is strengthened while the impact of the samples around the centre is weakened. Experiments are conducted ontwo common-used public benchmark datasets – Labeled Faces in the Wild (LFW) and YouTube Faces(YTF). Results show that the proposed method is competitive compared with the state-of-the-art methods, demonstrating the effectiveness of the proposed method

Wang, Hui

Queen's University Belfast Research Portal

A Minkowski Distance-based Generalisation Method for Improving Centre Loss for Deep Face Recognition

Ulster University's Research Portal

ulster.ac.ukA Minkowski Distance-based Generalisation Method for Improving Centre Loss for Deep Face RecognitionXin Wei, Hui Wang, Huan Wan and Bryan ScotneyAugust 29-31, 2018IMVIP 2018Irish Machine Vision and Image Processing Conference 2018Outline:1. Introduction2. The proposed method3. Conclusion and future works2/191. Introduction1. IntroductionFacial recognition system: a technology capable of identifying or verifying a person from a digital image or a video frame [1].1. Wikipedia contributors. (2018, August 27). Facial recognition system. InWikipedia, The Free Encyclopedia. Retrieved 09:49, August 28, 2018,from https://en.wikipedia.org/w/index.php?title=Facial_recognition_system&oldid=8567538554/191. IntroductionFig. 1. The basic framework of a face recognition system.5/191 IntroductionDeep learning-based methods Traditional methods Face recognition methods 6/192. The proposed method-- Minkowski Distance-based Centre loss (MC loss)2.1 Softmax Loss and Centre LossSoftmax Loss:where N is the batch size, K is the class number of a batch, fi∈ Rddenotes the feature of the i th sample belonging to the yi th class,Wj ∈ Rd denotes the j th column of the weight matrix W in the finalfully connected layer and bj is the bias term of the j th class.(1)8/192.1 Softmax Loss and Centre LossCentre Loss:where 𝜆 is the hyper-parameter for balancing the two loss functions. Total Loss:where 𝐶𝑦𝑖 denotes the class centre of the yi th class (2)(3)9/192.2 Minkowski Distance-based Centre loss (MC loss)where X = (x1, x2, . . . , xk ) and Y = (y1, y2, . . . , yk ) ∈ Rk . Therefore the nth power of the Minkowski distance of order n is:The Minkowski distance of order n between two points X and Y:(4)(5)10/192.2 Minkowski Distance-based Centre loss (MC loss)where n ∈ R>0. Typically, n is set to be 2, 3, 4, …MC Loss:where 𝜆 is the hyper-parameters for adjusting the impact of MC loss.Total Loss:11/19(6)(7)2.2 Minkowski Distance-based Centre loss (MC loss)Indicator:𝑓 ሶ𝑙𝑘−𝑐𝑘𝑓𝑗𝑘−𝑐𝑘𝑛,where ck is the kth feature of a class centre, fik is the kth featureof sample i, fjk is the kth feature of sample j. Sample i is on theedge of the class while sample j is near the class centre, and𝑓 ሶ𝑙𝑘−𝑐𝑘𝑓𝑗𝑘−𝑐𝑘> 1.When n = 2, MC loss deteriorates to Centre loss.When n > 2,𝑓 ሶ𝑙𝑘−𝑐𝑘𝑓𝑗𝑘−𝑐𝑘𝑛is greater than it was in the Centre loss.12/192.2 Minkowski Distance-based Centre loss (MC loss)13/192.2 Minkowski Distance-based Centre loss (MC loss)14/193. Conclusion and future worksConclusion• We proposed a Minkowski distance-based generalisationmethod for improving Centre loss for deep face recognition.• Experimental results on the LFW image dataset and the YTFvideo dataset show that the proposed method is highlycompetitive even compared with the state-of-the-art methods.16/19Future works• Design better loss functions to further enhance thediscriminative ability of features.• Explore the relationship between the network structuresand the loss functions, and find a stronger networkstructure.17/19Question and Answer18/19

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A Minkowski Distance-based Generalisation Method for Improving Centre Loss for Deep Face Recognition

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