The 10th International Conference on Management of Emergent Digital EcoSystems (MEDES'18)in Tokyo Metropolitan University, Minami-Osawa campus, Tokyo/Japan, September, 25-28, 2018
An IoT framewor k fo r f a ce d etectio n applied to a multimedia se n so r s y ste m Elie TAGNE FUTE University of B u ea University of Ds ch a ng
eliefute@ yahoo.fr
Lionel Landry SOP DEFFO University of B u ea University of Ds ch a ng
Emm anu el TONYE
University of Ya ound e I
[email protected]
[email protected]
ABSTRACT In r ecent years, we assist t o growing use of digital technology leading t o a demand fo r be tt e r procedures t o process multimedia information. Consequently we need t o build up a system enabling environmental monitoring by collecting da ta from sensing multimedia devices, including cameras and microphones: i t is t he no tion of Int e r ne t of Multimedia Things (IoMT). Among t he va riety of proposed appli-cations we have pedestr ians det ection, people counting, face de t ec t ion /r ecogni t ion ... Even if i t is tr ue t ha t resul ts a re impressive in general and in face det ect ion in pa rticula r, t he r e a re still difficulties fo r t hei r use in real time applica-tions. T ha t is why many specialists o r ient a t e t hei r research on t his path. We proposed an IoM T framewo rk fo r fo r face de t ect ion t ha t uses multimedia sensors. T he proposed ap-proach has t h r ee major contributions. F i r s t ly we propose an IoM T model fo r face detection, secondly we proposed a joint convolutional neural netwo rk model reinforced by CReLUs modules and finally we propose and implementation t o real time applications.
Categories and Subject Descriptors Computer vision [Image processing]: Artificial neural netwo rks—Intelligent networks
General Terms Delphi t heo r y
Keywords IoMT, convolutional neural netwo rk, face det ection, model, CReLU module
1. INTRODUCTION A human brain is wired t o do objects de t ection and recogni tion automatically and instantly. Computers however a re no t capable of t his kind of high-level generalization, so we
need t o t each them how t o do each st ep in t his process separately. Face det ec tion t o t he o t he r hand, can be r egarded as a specific case of object-class det ec t ion where t he t ask is t o find t he locations and sizes of all objects in an image t ha t belong t o a given class. Ever since t he seminal work of Viola e t al.[24], wit h some improvements [27] t he boosted cascade wi t h simple fea tur es becomes t he mos t popular and effective design fo r practical face det ection. Most of t hem follow t he boosted cascade framework wi t h more advanced fea tures which help t hem t o cons tr uc t a more accura te binary classifier a t t he expense of ex tr a computa tion. In 2005, ano t her popular method was invented called Histogram of Oriented Gradient s [16] o r just HOG fo r sho rt . I t is based on evaluating well-normalized local histograms of image gradient o rient a tions in a dense grid. T he basic idea is t ha t local object appearance and shape can often be characterized r a t he r well by t he distribut ion of local int ensity gradient s o r edge directions, even wi t hou t precise knowledge of t he corresponding gradient o r edge positions. Now comes Convolutional Neural Ne two r k (CNN) applied t o face det ect ion because compared wi th t he previous hand-craft ed fea tures, CNN can automat ically learn fea tures t o capt ur e complex visual variations by leveraging a large amount of training da t a and i t s t es t ing phase can be easily paralyzed on G P U cores fo r accelerat ion. This paper pr esents a framework fo r face de t ection applied t o a multimedia sensor system and has t h r ee main contributions. F i r s t ly we propose an IoM T model fo r face det ec tion, secondly we proposed a joint convolutional neural netwo rk model reinforced by CReLUs modules and finally we propose an implementation t o real t ime applications. T he r es t of t he paper is organized as follows, Section 2 discusses on t he previous work done on face det ection especially ones which use neural netwo rks. In Section 3, t he details on t he proposed framewo rk a re exposed. In section 4, t he tr aining methodology is presented. Section 5 presents t he implementa tion, analysis and r esult s int e r p r e t a t ions included. Finally, Sect ion 6 concludes t he wo rk by doing an appraisal and by proposing amelio rat ion perspectives.
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Face de t ection can be classified according t o t wo classes of techniques namely feature-based approach which consist of low-level visual fea t ures extract ion (color, edges, etc.) followed by det ec tion of face fea t ures (mound, lips, nose, eyes, etc.) and i t s rela tive placements and image based
2.1
Features based face detection
The 10th International Conference on Management of Emergent Digital EcoSystems (MEDES'18)in Tokyo Metropolitan University, Minami-Osawa campus, Tokyo/Japan, September, 25-28, 2018
T hese approaches use facial fea tures t o cons tr uc t t hei r det ect ion model. If we r efer t o t he wo rk of [9], those approaches can be regrouped into:
2.1.1
Low level analysis detection
I t segments t he visual fea tures by using pixels properties, gray scale level and mo tion information. T his can t her efo r e been used in edge based techniques as in [4] where t he matches edge labels t o face model fo r verifica tion.
2.1.2
Features analysis
Here, information about face a re used t o remove t he ambiguity produced low level analysis. T his as has mention Urvashi in [1] allows less prominent fea tures t o be hypothesized.
2.1.3 Active shape model When we want t o define actual physical and higher level appearance, we use t his model. T im Coo t es e t al. [22] have developed and use t he model t o int e r ac t wi t h local image deforming t o t ake t he shape of t he fea tur es. T his approach mainly consists of looking in t he image around each point fo r a be tt e r position fo r t ha t point and updates t he model pa r amet e rs t o bes t ma tch t o t his new found position.
2.2
Image based techniques
T he problem wi th t he previous method is t ha t , explicit modeling of facial a re troubled by t he unpredictability of face appearance and environmental conditions. T hey t herefo r e need more robust techniques, capable of performing in unfriendly environments, such as det ec ting multiple faces wi th clutter-int ensive backgrounds. T he basic approach in recognizing face pa tt e r ns is via a training procedure which classifies examples into face and non-face p r o t o type classes. Comparison between these classes and a 2D int ensi ty a rray (hence t he name image based) ex tr ac t ed from an input image allows t he decision of face existence t o be made. Most of t he image-based approaches apply a window scanning technique fo r det ec ting faces. T he window scanning algo ri thm is in essence just an exhaustive search of t he input image fo r possible face locations a t all scales, bu t t he r e a re varia tions in t he implementation of t his algo ri thm fo r almost all t he image-based systems. T hey a re classified according t o t wo main families namely linear subspace methods and neural netwo rk methods.
2.2.1 Linear subspace methods T hese t echniques r efer generally t o principal component analysis (P CA) [14], linear discriminated analysis (LDA) [26], and facto r analysis (FA). Pr incipal component analysis (P CA) is a t echnique fo r reducing t he dimensionality of large da taset s, increasing inter pr etabili ty bu t a t t he same time minimizing information loss. I t does so by cr ea ting new unco rrela t ed variables t ha t successively maximize variance. Linear Discriminated Analysis (LDA) t o t he o t he r hand is most commonly used as dimensionality reduction technique in t he pre-processing s t ep fo r pa ttern-classifica tion and machine learning applications. T he goal is t o projec t a da taset onto a lower-dimensional space wit h good classes po rtability in o r de r avoid ove r-fi tting (curse of dimensionality) and also reduce computational costs.
2.2.2 Statistical methods
Apa rt from linear subspace methods, t he r e a re several o t he r s t a t ist ical approaches t o face det ect ion which a re Systems based on information t heo ry, suppo rt vecto r machine and Bayes decision rule. Huang [3] proposed a syst em based on Kullback r ela tive informat ion called Kullback divergence which is a non-negative measure of t he difference between two probability density functions P Xn and MXn fo r a random process Xn. I t is based on an earlier wo rk of maximum likelihood face det ec t ion [2]. Osuna e t al. [8], proposed a suppo rt vect o r machine (SVM) [23] applied t o face det ect ion which follows t he same framewo rk as t he one developed by Sung and Poggio [21] t ha t scan input images wit h a 19X19 window. Here a SVM wit h a 2nd-degree polynomial as a kernel function is trained wit h a decomposition algo rithm which guarantees global optimality. Finally Schneiderman and Kanade [19], [20] describe two face de t ec t o r s based on Bayes decision rule which is present ed as a likelihood r a t io t es t using random variables. If t he likelihood r a t io is g r ea t er than t he r ight side, t hen i t is decided t ha t an object (a face) is pr esent a t t he cu rr ent location.
2.2.3 Neural network methods Neural netwo rks based techniques a re popular fo r pa tt e r n recognition and a re inspired by t he human brain. T he fi r s t advanced neural approach which r epo rt ed result s on a large, difficult da t ase t was by Rowley e t al. [10]. T hei r syst em inco rpo ra tes face knowledge in a retinally connected neural netwo r k designed t o look a t windows 20 X 20 pixels. One of t he r ecent approach is a convolutional neural netwo r k (CNN) based de t ection method proposed by Girshick e t al. [17] which has achieved t he s t a t e of t he a rt resul t on VOC 2012. I t follows t he recognition using regions paradigm. I t generates ca tego ry independent region proposals and ex tr ac t s CNN fea tures from t he regions. Then i t applies class-specific classifiers t o recognize t he object ca t ego ry of t he proposals. O t he r methods have followed such as Chen e t al. [5] who propose t o use t he shape indexed fea tures t o jointly conduct face det ection and face alignment Zhang e t al. [25] and P a r k e t al. [6] adopt t he mul ti resolution idea in general object det ection. A more r ecent framewo rk includes t he use of a deep cascaded mul ti-t ask [7] framework which exploits t he inher ent co rr ela tion between det ect ion and alignment t o boost up t hei r performance. Our proposed model is mostly based on t his last model. T he models proposed in all above methods a r e characterized by two phases named t he tr aining phase and t es t phase. In t he training phase t he system learn how t o recognize known fea t ures which specific developed techniques. Once i t is done we can now use t he model t o t es t on unknown da ta and le t t he training system de t ec t s automatically t he needed fea tures.
3. THE PROPOSED FRAMEWORK T he overall system consist s of four different pa rt s which go from t he acquisition t o t he det ect ion. T his a rchit ect ur e is present ed on Figur e 3.4
3.1 The Multimedia sensors In t his pa rt , we find any senso r device capable of collecting video information in r eal t ime. This includes cameras, sma rt phones, comput er camera, raspberry camera ... I t should be no t ed t ha t a video a jus t a collections of images commonly called frames, consequent t he acquisition device
The 10th International Conference on Management of Emergent Digital EcoSystems (MEDES'18)in Tokyo Metropolitan University, Minami-Osawa campus, Tokyo/Japan, September, 25-28, 2018
F igur e 1: Frame work d ete c t ion s y s te m . localization. We t he r efo r e use t he same ma thema t ical formula fo r these tasks.
4.1 F igur e 3: P ropos e d C Re lu modul e. needs t o have an acceptable number of frames t ha t i t can capt ur e per second such as 30fps fo r example.
3.2 Pre-p rocessing Unit Here all pr e operations needed as t o be perfo rmed . T his include image denoising, image resizing, image filtering... T his is t o ensure t ha t t he quality of t he ou t pu t r esul t is acceptable. In addition a bad image can lead t o poor performances fo r a processing algorithm.
3.3 The Proposed model As mention earlier, our model is based on t he model proposed in [7] and is t her efo r e cons t i t u t ed of t h r ee neural netwo rks namely P -Ne t-imp, R-Net-imp and ONet.
3.3.1 The P-Net-imp neural network I t is a fully convolutional network, called Proposal Netwo rk ( P -Ne t ), and used t o obtain t he candidate facial windows and t hei r bounding box regression vecto rs. T hen candidates a r e calibrated based on t he estima t ed bounding box regression vecto rs. Finally a non-maximum suppression (NMS) is employed t o merge highly overlapped candidates. T he fo rmer a r chi t ect ur e of t his netwo r k is presented in F igu r e 3.3.1. As i t is commonly known t ha t t he deeper t he netwo rk, more accura te is t he r esul ts, we increase t he depth of t he previous a r chi t ec t u r e by pu tting in a proposed version of CRelu (see F igur e 3.3.1) module which replaces t he normal ReLU and P ReLU activa tion functions. T his is wi t h t he knowledge t ha t CReLU increases t he quality of t he r esul t as proven in [11]. T his gives r ise t o t he model shown in F igure 3.3.1.
3.4 The R-Net neural network All candidates a r e fed t o ano t her CNN, called Refine Netwo rk (R-Net), which fu rt he r reject s a large number of false candidates, performs calibration wit h bounding box r egression, and conducts NMS. T his is shown in Figur e 3.4
3.5 The R-Net neural network T his neural netwo rk is t he same as t he one in [7], and i t aim is t o identify face regions wit h more supervision. In pa rticula r, t he ne two rk will ou t pu t five facial landmarks positions. F igur e 3.5 shows all t he ne two r k components.
4. TRAINING METHODOLOGY Le t us recall t ha t t he proposed model is tr ying t o perform t he same t ask as t he original model which a re face/non-face classification, bounding box regression, and facial landmark
Face classification
Here, t he learning objective is fo rmulated as a two-class classification problem. Fo r each sample, we use t he crossentropy loss Where t he probability is produced by t he net wo r k t ha t indicates sample being a face and t he no t a tion deno t es t he g r ound- tr u t h label.
4.2 Bounding box regression Here a prediction of t he offset between a window and i t nea r est ground tr u t h (i.e., t he bounding boxes left, top, height, and width) is made. T he learning objective is formulated as a regression problem wi t h t he Euclidean loss fo r each sample: Where is t he regression t a r ge t obtained from t he netwo rk and yi box is t he g r ound- tr u t h coordinate. T he r e a re four coordinates, including lef t t op, height and widt h, and t hus .
4.3
Facial landmark localization
Similar t o bounding box r egression t ask, facial landmark det ect ion is formulated as a regression problem and we minimize t he Euclidean loss: Where is t he facial landmark coordinat es obtained from t he netwo rk and is t he ground tr u t h coordinate fo r t he i-th sample. T he r e a re five facial landmarks, including lef t eye, r ight eye, nose, lef t mouth co rner, and r ight mout h corner, and t hus .
4.4
Pnet-imp training phase
Before talking about t he tr aining phase le t us highlight a fact. I t is impo rt ant t o no tice t ha t in t he original methodology; t he training is done six t imes t ha t is twice per neural netwo rk. A t fi r s t a normal training is done fo r P Ne t neural netwo r k using da ta collected from a da t ase t (WIDER FACE) in our case. T hen t his stage genera tes ano t her se t of da t a which is used t o r e tr ain t he same neural netwo rk. T his process is called fine tuning. However t his second process t akes a lo t of t ime and needs a lo t of resources and t he commonly used approaches a re G P U r equi rement s o r cloud computing techniques. T ha t is why we t hought of proposing a model t ha t will enable us t o avoid t he second stage of training fo r t he result s obtained from t he fi r s t stage have high level of accuracy and can t her efo r e be used in some applications. In this paper we s t a rt ed by modifying t he fi r s t neural net wo r k ( P Ne t netwo rk) and see i t s effect on t he resul ts and we intend t o do t he same fo r t he remaining networ ks (RNet and ONet) in t he fu t u r e work. We the r efo r e perform t he P Ne t training using t he following steps:
4.4.1 Collect appropriate data set Here we select a da ta se t t ha t cont ains labeled face wi th t hei r corresponding bounding boxes, fo r t ha t we choose t o use WIDER FACE [18] da t ase t fo r i t is one of t he most used
The 10th International Conference on Management of Emergent Digital EcoSystems (MEDES'18)in Tokyo Metropolitan University, Minami-Osawa campus, Tokyo/Japan, September, 25-28, 2018
F igur e 2: P N e t convolutionnal mod e l.
F igur e 4: P ropos e d P - Net -imp n etwork .
F igur e 5: R N e t convolutionnal mod e l.
F igur e 6: O N et convolutionnal mod e l.
The 10th International Conference on Management of Emergent Digital EcoSystems (MEDES'18)in Tokyo Metropolitan University, Minami-Osawa campus, Tokyo/Japan, September, 25-28, 2018
Tabl e 1: Param ete rs us ed in t h e training process Values P a r ame t e r s Number of i t e r a t ions 120000 Ini tialization method Xavier method Propaga tion algo ri thm S tochastic gradient descent (SGD) Momentum 0.9 Weigh decay 0.0005 Ba tch size 100 Learning r a t em 0.0001 da ta se t t o de t ec t bounding box
4.4.2 Dividing images into categories Here we divide our da t ase t into t h r ee catego ries; positive, pa rt and negative images based on t he IoU algo rithm [15] which s t a t es t ha t an image is said t o be positive if t he intersection over union r a tion is g r ea t e r t han a se tt le maximum value, pa rt if i t is between t he maximum value and a se tt le minimum value and nega t e otherwise.
F igur e 7: Dete c te d face using t h e original mode l Source: F D D B da t as et [12]
4.4.3 Training of the model Here we tr ain t he proposed model using a neural netwo rk library such as tensorflow, t heano, caffe, t o r ch e t c. bu t since t he idea was t o propose a method based on C P U t ha t do no t depends on any G P U, we choose t o use t he C P U instance of caffe .
5. IMPLEMENTATION AND RESULTS ANALYSIS We choose Caffe [13] t o implement our solution. I t is Caffe a deep learning framewo rk made wit h expression, speed, and modula rity in mind. I t is developed by Berkley AI Research (BAIR) and by community contributo rs. T he choice is motivated by: Expressive a r chi t ect ur e Extensible code Speed Community I t is w r i tt en in C / C + + and has a python interface. T he pa r amet e rs used t o tr ain our P NE t model a re listed in Table r eftab1 We t ook a t random some images in F DDB da t ase t [29] t o t es t our model and t his gave rise t o t he following r esul ts. F igur e reffig6, F igur e reffig8 and Figur e reffig10 pr esent t he r esul ts of faces de tect ed by t he original model, where t he P ne t has been trained wi t h our proposed training method-ology and t he remaining netwo rks (RNet and ONet) use t he existing pre-trained models. On t he o t he r hand F igur e r ef-fig7, F igur e reffig9 and Figur e reffig11 pr esent t he r esul t s of faces det ec t ed by t he our proposed model. I t can be eas-ily seen t ha t t he r esul t s of our model is more accura t e as i t de t ec t t he whole face while t he original model t end t o de t ec t just a pa rt of t he face. T his shows how t he CReLUs enable t o obtain t he good resul ts quickly. T his is be tt e r observed on loss curves (F igur e r effig12, reffig13, reffig14 and reffig15), where loss reaches 0 in t he proposed mode which is no t t he case on t he existing model. T ha t is why in t he original method t he aut ho rs fined t hei r model with ano t her training stage t o improve t he resul ts while in our model, t he r esul ts is already acceptable a t t his stage. F igur e reffig12 shows t he evolution of losses during t he training phase and t he t esting phase fo r t he two models. T he scheme is t he same t ha t is i t increases and decreases. Nevertheless, t he r e is a difference. While in t he original method loss decreases t o a minimum value g r ea t e r t han 0,
F igur e 8: Dete c te d face using t h e proposed model Source: F D D B da t as et [12]
F igur e 9: Dete c te d face using t h e original mode l Source: F D D B da t as et [12]
The 10th International Conference on Management of Emergent Digital EcoSystems (MEDES'18)in Tokyo Metropolitan University, Minami-Osawa campus, Tokyo/Japan, September, 25-28, 2018
F igur e 10: Dete c te d face using t h e proposed model Source: F D D B da t as et [12]
F igur e 13: Loss evolu t ion during training of t h e original model
F igur e 14: Loss evolu t ion during training of t h e proposed model
F igur e 11: Dete c te d face using t h e original model Source: F D D B da t as et [12]
i t reaches 0 in t he proposed model. Since t he aim of t he training process is t o minimize losses which is t he difference between t he expect ed r esult s and obtained resul ts, we can t he r e said t ha t our model outperfo rms t he exist ing one in t e r ms on losses. This can be clearly seen on Figur e reffig12, Figu r e r effig13, Figur e r effig14 and F igur e reffig15 r espectively. Here i t is clearly seen t ha t during t he training phase t he minimum loss never reaches 0 fo r t he original model (F igu r e reffig12) which is no t t he case in t he proposed model (F igur e reffig13). T he second curve r eaches 0 fo r i t touches t he x axis many t imes during t he whole training. T he same observations can be made on F igur e reffig14 and F igur e reffig15. T he curve never reaches in t he case of t he original model bu t succeed in dong in t he propose model. T he model can ther efo r e be recommended fo r some neural ne two rk applications.
5.0.4 Application in real time
F igur e 12: Dete c te d face using t h e proposed model Source: F D D B da t as et [12]
In t his section we pr esent t he execution of t he framework in a real t ime situa tion. F igur e reffig16, F igur e reffig19, F igur e reffig17 and F igur e r effig18 give some snapshots of t ha t execution in real t ime where t he multimedia sensor used here is t he camera of t he laptop. A t t he left we have t he execution using t he existing model wi t h pre-vious mentioned trained t he training methodology and a t t he left t he execution using t he proposed model using t he same training
The 10th International Conference on Management of Emergent Digital EcoSystems (MEDES'18)in Tokyo Metropolitan University, Minami-Osawa campus, Tokyo/Japan, September, 25-28, 2018
F igur e 15: Loss evolu t ion during te s t ing of t h e original model
F igur e 17: Dete c t ion in re al t im e
F igur e 16: Loss evolu t ion during te s t ing of t h e proposed model methodology and da tase t. I t can clearly be seen t ha t t he proposed model out perfo rmed t he existing one in t e r m of t he accuracy of t he r esul ts. T he face det ec t ed by t he proposed model is more precise (F igur e reffig16, F igur e reffig19) compare t o t he face obtained from t he original model. While in t he fi r s t si t ua tion F igur e r effig16, t he rectangula r drawn in our si tua t ion is thicker than t he one obtained from t he existing model, t he second si t ua t ion (F igure reffig19) shows how t he de t ec t d face is been cu t a t t he level of t he chin fo r t he existing model, t he rectangle det ec t ed by our model covers t he whole face. Fu rt he r mo r e in many si tua tions, t he proposed model do no t de t ec t t he face when i t is t u r ned in some di rections ( F igur e r effig17 and F igur e r effig1). This show once more t he benefit of our proposed a r chi t ect ur e.
F igur e 18: Dete c t ion in re al t im e
6. CONCLUSION We presented in this paper a light joint convolutional neural netwo rk cascade model fo r face de t ection fo r C P U. The model is based on a mul ti t ask convolutionnal neural netwo rk t ha t jointly de t ec t s face and landmark positions. T he model has been reinforced by CRelu modules and we showed how t he r esul t s were be tt e r fo r t he propose model exactly de t ec t t a r ge t face. Fo r fut ur e wo rk we intend t o extend t his improvement t o t he t wo remaining sub models (RNet ant ONet) so as t o genera te global training da t a t o fine tuning t he model. Fu rt he r mo r e since t he generation of new da ta t o fine tuning t he proposed model takes a lo t of time (more than two months a t time) we intend now t o use G P U t o fast e r i t execution t o fast er i t execution.
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F igur e 19: Dete c t ion in re al t im e
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F igur e 20: Dete c t ion in re al t im e
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