There are numerous large-scale biomedical and pharmacological research projects to study Alzheimer’s Disease (AD), and potential drugs and therapeutic interventions to improve this severe disease. Of significant importance are life quality of AD patients.In particular, AD patient’s ability to recognize intimate family members and nurses’ faces largely decides their life quality. The broad objective of this research is focused on providing methods to determine the extent of disease progress from the viewpoint of recovering as much cognitive ability as possible.Specifically, this research would computerize the AD patient’s diseased brain and retrained the brain with focus on recovering the visual recognition ability of family member and medical care personnel. Likewise, potential recommendations for the patients’ family members and others who interact with the patients, in order to help improve quality of life and daily interactions

Zhang, Yuxuan

English

Electronics Science Technology and Application (E-Journal)

ORIGINALARTICLEElectronics Science Technology and Application Volume 6 Issue 1 | 2019 | 29A Computational Model of Quantitatively Measuring the Alzheimer’sDisease Progression in Face IdentificationYuxuan Zhang*University of California, San Diego 9450, Gilman Drive. E-mail: 695589889@qq.comAbstract: There are numerous large-scale biomedical and pharmacological research projects to study Alzheimer’sDisease (AD), and potential drugs and therapeutic interventions to improve this severe disease. Of significantimportance are life quality of AD patients. In particular, AD patient’s ability to recognize intimate family members andnurses’ faces largely decides their life quality. The broad objective of this research is focused on providing methods todetermine the extent of disease progress from the viewpoint of recovering as much cognitive ability as possible.Specifically, this research would computerize the AD patient’s diseased brain and retrained the brain with focus onrecovering the visual recognition ability of family member and medical care personnel. Likewise, potentialrecommendations for the patients’ family members and others who interact with the patients, in order to help improvequality of life and daily interactions.Keywords: Computational Model; Alzheimer’s Disease; Face Identification1. IntroductionAlzheimer’s disease (AD) is one of the mostprevalent degenerative disease of brain, caused bycomplex changes in brain with damaged neuron(Alzheimer’s Association). Severe AD could lead todementia, having difficulty in remembering things.While the irresistible fact of aging has been proven to bethe greatest risk factor causing AD, this disease become anonnegligible problem for everyone. AD is a progressivedisease and is generally divided into three stages: mild(early stage), moderate (middle stage), severe (late stage).During the late stage, AD patients are unable to respondto environment and finally control movement. Cognitiveskills and memory system are continuously deteriorating,so forgetting familiar faces and failing to identifyfamilies are common in this stage. Dementia andamnesia symptoms are common and severe for severeAD patients.Despite years of devotion into AD field, there is stillno way to prevent progression or a cure for the disease. Aresearch on therapies for AD by Fluidic Analytics revealsthat only 4 out of 146 drugs are effecting in treating ADsince 1998. The estimated cost in 2015 to society isabout $818 billion, including caring AD patients anddevotion to AD research. More astonishingly, the globalpopulation of AD patients would increase up to 135million by 2050, since the aging population also increaserapidly (Fluidic Analytics 2019).Although numerous researchers have approached toimprove Alzheimer’s disease, a family tragedy and asocial dilemma, no healing for AD have been discoveredand the life quality of AD patient remain hard. Quality oflife (QQL) is a well-established measurement for ADpatient’s life quality, including cognitive functioning,ability to perform daily living, ability to engage inactivities, and a balance between positive and negativeemotion (Lawton MP 1994).Most researches related to Alzheimer’s disease areCopyright © 2019 Yuxuan Zhangdoi: 10.18686/esta.v6i1.93This is an open-access article distributed under the terms of the Creative CommonsAttribution Non-Commercial License(http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium,provided the original work is properly cited.30 | Yuxuan Zhang Electronics Science Technology and Applicationseeking for an early diagnosis. They hope to discoverpotential AD patients as early as possible. Liu. & Liu &Cai [2014] and Ding.etc. [2018] offer two differentmodel of classifying and pre-diagnosing AD based ontechnique of deep learning and convolutional network.This work is motivated by the rapidly growingpopulation of AD patients, the severe life qualitiespatients and their families are suffering, and the lack ofpractical research on how to efficiently improve patients’life quality.2. BodyFace perception is the term referring to howhuman brain recognize and interpret faces and is also oneof the most pinnacled inverse problem. Numerousneuropsychological experiments had revealed thathuman brain works the same as a gigantic neural network.Some recent studies utilizing deep learning areapproaching to building the network. However, up totoday, still no reliable researches are qualified to be anauthoritative and comprehensive computational model ofthis abstruse memory system in human brain. To solvepractical problems related to decipher the face perception,some researchers categorize it into 6 components basedon many studies of face recognition (Leopold & Rhodes2010). Identity, emotional expression, eyeappearance(gaze), attraction, development and neuralspecialization constitute this cognitive process of gainingmultidimensional information from face. Over the lastfew decades, countless researches have studied differentparts of face recognition, and many profound algorithmsand computational models have proliferated. Around theend of 20 century, active shape models (ASM) areinvented and provide a very forerunner technique forface recognition in detecting facial features (Cootes1995). ASM offers an alternative way of describing faceimages. Less than 10 years later, a significant frameworkis designed with a new image representation, the“Integral Image”. It greatly improved the efficiency andthe accuracy of face detection and promoted theapplication of face recognition (VIOLA & JONES 2004).Limited to current capability to model faceperception and considering our goal to ameliorate ADpatients’ life condition, we choose to build acomputational model simulating how AD patientsrecognize faces and distinguish their identities.We take the famous model by Turk and Pentland[1991] in which eigenfaces are applied to be a linearcombination of facial features. Following the paper andSirovich and Kirby [1987] which shared a similar ideausing ‘eigenpicture’ , the steps of building our modelcould be separated into two parts:2.1 Computing the eigenfaces (part 1)Step 1: Collect set of size n face images of the ADpatient’s family members and medical personnel be thetraining set. Similar background, such as white wall, andsimilar size of centered faces in picture is preferred dueto the limitation of this model.Step 2: Represent each image as a vector consistingof 128 × 128 = 16384 pixels. Thus, a 16384 × nmatrix L is created.Step 3: Normalize the matrix L by subtracting theaverage face from each face, which is the collection of‘caricatures’ defined by Sirovich and Kirby.Step 4: Find the eigenvector and eigenvalues of thecovariance matrix L' = LLT.Step 5: Choose M eigenvectors, namely principalcomponents, with the highest associated Eigenvalues.The M images is the ‘face space’. Each face in thetraining set is a projection of its caricature onto theM-dimensional ‘face space’ with the average face, andalso is a linear combination of the principal components.2.2 Recognize faces (part 2)Step 1: Normalize the new face and project themonto the ‘face space’.Step 2: Choose a threshold θe defining themaximum allowable distance from face class andcalculating the distance ei between the new face and theface class. Compare the threshold and the calculateddistance. Among the methods of evaluating distance,common Euclidean distance is easy and simple;Mahanlanobis distance and angle-based distance performthe best in recognition process (Perlibakas 2003).Step 4: If ei < θe, this new face should berecognized as a family member or nurses of this ADPatient. The face should be added into the face class andthe face space would be recomputed, which are steps forconstantly learning model.Slightly modifying Turk and Pentland’s model, weElectronics Science Technology and Application Volume 6 Issue 1 | 2019 | 31have a primary computational model about how ADpatients could correctly recognize faces of their intimaterelatives and medical staff. This model is simple,efficient and accurate for face recognition, whichhad been verified by experiments done by Turk andPentland. Though, as raised in the paper, compared tosome latest model for face recognition, this model is onlyeffective in a constrained environment (small set). Underour case, this model provides a very practical andefficient solution.Because AD patients meet the most problem ingetting to forget or being confounding about who aretheir acquaintances, the treatment should merely helpthem recognize the identity of their intimate familymembers and some medical personnel from a large butunimportant population. Hence, the size of the set ofvalid faces is small, and the difficulty of recognition isnot that high compared to other social dilemmas, such ascriminal identification. In our case, we could reasonablyrequire patients’ families to present the most explicitimage of face in front of them. While other popularmodels for face recognition may be more advanced, thiseigenface model emphasizing significant features offaces best fits our situation.2.3 PerturbationTo update the primary model for AD patients, weneed to perturb some steps in the algorithm to simulatethe brain with amnesia.A well-confirmed statement is that ADpatients’ brain fails to do some required analysis of theimage it received. AD patients at stage three have severedifficulty in focusing and concentrating. Hypothetically,AD patients’ brain cannot differentiate betweenimportant and unimportant features from receivedinformation. Equivalently, under the approach ofeigenfaces with taking M principal components, ADpatients may miss the principal components containingthe most information. The dysfunctional brain couldincorrectly take another principal component as the mostuseful one. For example, three principal componentscontain 1%, 2%, and 97% of information of faces, andthe brain consider the first two components mostimportant. Besides, the brain could fail to include asmany components as needed. For example, four principalcomponents contain 25% each, and the brain is only ableto have maximum two components with only 50% ofinformation.2.4 Perturb the model (part 3)Step 1: Change the rule of picking eigenvectors(principal components) in Part 1 Step 5. (1) Randomlypick M eigenvectors of matrix L'. (2) Pick M/2eigenvectors with highest eigenvalues of matrix L'. (3)Pick 2 eigenvectors with highest eigenvalue of matrixL' M = 2 . Step 2: Repeat the other steps listed above.Step 3: Compare the accuracy of testing result on newfaces.After the three-parts steps, we have thecomputational model for AD patients having difficulty inrecognizing faces. The accuracy of this computationalmodel could be easily verified, since the only change ofthis AD model from the Eigenface model by Turk andPentland is the rule of selecting principal components.Nonetheless, the reliability of this AD modelin biological field require further some experiments tosupport that real AD patients perform the same as the ADmodel when recognizing specific identities of faces.One suggestive experiment might apply the visualmismatch negativity (vMNN) which is a component ofN2 visual even-related potential (ERP) and isobserved by researchers displaying abnormal increaseover a specific measurement epoch (Tales 2008).Quantitative Measurement/Recovering:Now we have a simple and valid model as weneeded. To transform the model compatible forquantitatively measuring the extent of disease progressfrom the perspective of recovering cognitive ability, weneed to consider some clinical practices which have beenverified by pharmacological experiment. Eye-contacttraining had proved to be one qualified and populartreatment against Alzheimer’s disease. A recent studytesting eye contact effect on face identification for ADpatients, aging people and young adults claims that theeye contact effect decreases as elder age. It providesrobust behavioral evidence supporting that for ADpatients, performance of recognizing faces could beimproved by taking ‘direct-gaze’ effect, equivalently theeye-contact training in clinical treatment (Lopis 2019).Another neuroscientific research through functionalresonance imaging (fMRI) illustrates that AD and mildcognitive impaired (MCI) patients have an impairment in32 | Yuxuan Zhang Electronics Science Technology and Applicationinhibitory control of saccadic eye-movement, which is arapid special eye-movement pattern (Alichniewicz,Brunner, Kl nemann, & Greenlee, 2013). It implied thatthe patients’ brain have function deficit in controllingeye-movement, which directly correlates to faceidentification. AD patients’ eye-movement is more likelyto locate around nose rather than eye (Firestone 2007).We modify the model to compulsively extract mosteye-central information from images.2.5 Recover the model using eye-contacttraining (part 4)Step 1: Locate eigenvectors telling the mosteye-central information. Since the size of our image setis small, the maximum number of eigenvectors is alsosmall. We could examine each eigenvector orderly tohave the eye related information. The method ofexamination would evolve technique to recognize ‘eye’from images and active shape model is a good choice.Step 2: Choose the selected eigenvectors and finishthe process of eye-contact training.Step 3: Repeat the following steps and get theresults.By computerizing the eye-movement treatment andadding it into the AD model, we have a qualifiedcomputational model that could predict the theoreticalextent of disease progression and the numeric result ofthe model is the quantitative scoring for the treatment’srecovering effect.3. DiscussionOur computational model simulating how ADpatients recognize faces and how much disease wouldprogress after applying a clinical treatment. The overallmethods are built on the basis of other’s successfulresearching result and theoretical validation. Furtherdevelopment need to validate and update this primarymodel and be more comprehensive and exhaustive. Somecomplex details of AD patients are ignored in this project,such that the disease could be categorized into sevenstages, while one therapy might perform diversely ondifferent stages. Also, the method of locating eye-centralpictures require more attention.On another side, this model presents an innovativeview of solving AD and amnesia problems. It doesvalidly reveal the possibility of analyzing and solving theAD patients’ amnesia problem, tightly relating to mildcognitive impaired patients (MCI) and memory-systemimpaired robots. Moreover, it does provide a simple,effective and practical approach to this problem from theviewpoint of face recognition on identity instead of faceperception. It focuses on modelling how eye-movementtraining could help prevent the Alzheimer’s diseaseprogression. In application, with respect to an AD patientor another cognitive impaired patient with similardiagnosis, doctors or families are able to more directlyunderstand to what extent an insignificant dysfunctionin brain effect the patient’s ability of recognition, and towhat extent a specific therapy would benefit the patient.With enough further researches on this topic, each patientis able to have their unique disease model. Besides, thecorresponding physician could forecast and compare thecurative effect of different therapies through using theupdated model. The model could also be a usefulapproach for validating a new-coming drug or non-drugtreatment.This new practical viewpoint is an accessible,efficient and beneficial way of considering Alzheimer’sdisease and amnesia, especially compared with arduoustopic on face perception and memory-system modelling.This computational model applies directly on improveAlzheimer’s disease patients’ life quality in faceidentification. It could not only facilitate physician butalso encourage families.AcknowledgementProfessor AssadiReferences1. https://www.alz.org/alzheimers-dementia/stagesAlzheimer’s Association.2. https://www.fluidic.com/resources/researching-therapies-for-alzheimers-disease/ Fluidic Analytics.3. Lawton MP. Quality of life in Alzheimer disease.Alzheimer Dis Assoc Disord. 1994; 8 Suppl 3138-150. PMID: 7999340.4. Liu S, Liu S, Cai W, et al. Early diagnosis ofalzheimer's disease with deep learning. 2014 IEEE11th International Symposium on BiomedicalImaging (ISBI), Beijing, 2014, p. 1015-1018.5. Ding Y, Sohn JH, Kawczynski MG, et al. A deeplearning model to predict a diagnosis of Alzheimerdisease by using 18F-FDG PET of the brain.Radiology 2018; 290(2): 456-464.https://doi.org/10.1148/radiol.20181809586. Leopold DA, Rhodes G. A comparative view ofElectronics Science Technology and Application Volume 6 Issue 1 | 2019 | 33face perception. J Comp Psychol. 2010; 124:233–251. https://doi.org/10.1037/a0019460.7. Cootes TF, Taylor CJ, Cooper DH, et al. Activeshape models-their training and application.Computer Vision and Image Understanding Volume1995; 61(1): 38-59.https://doi.org/10.1006/cviu.1995.1004.8. Viola P, Jones MJ. Robust real-time face detection.International Journal of Computer Vision 2004; 57:137.https://doi.org/10.1023/B:VISI.0000013087.49260.fb.9. Turk M, Pentland A. Eigenfaces for recognition. J.Cognitive Neuroscience 1991; (3)1: 71-86.http://dx.doi.org/10.1162/jocn.1991.3.1.71.10. Sirovich L, Kirby M. Low-dimensional procedurefor the characterization of human faces. Journal ofthe Optical Society of America A 1987; 4(3):519–524. https://doi.org/10.1364/JOSAA.4.00051911. Perlibakas V. Distance measures for PCA-basedface recognition. Pattern Recognition Letters 2004;(25) 6: 711-724.https://doi.org/10.1016/j.patrec.2004.01.011.12. Tales T, Haworth J, Wilcock G et al. Visualmismatch negativity highlights abnormalpre-attentive visual processing in mild cognitiveimpairment and Alzheimer's disease.Neuropsychologia 2008; 46(5): 8, Pages 1224-1232.https://doi.org/10.1016/j.neuropsychologia.2007.11.017.13. Lopis D, Baltazar M, Geronikola N. et al. Eyecontact effects on social preference and facerecognition in normal ageing and in Alzheimer’sdisease. Psychological Research. 2019; 83: 1292.https://doi.org/10.1007/s00426-017-0955-6.14. Alichniewicz K, Brunner F, Kl nemann HH, et al.Neural correlates of saccadic inhibition in healthyelderly and patients with amnestic mild cognitiveimpairment. Front. Psychol 2013; 4: 467.https://doi.org/10.3389/fpsyg.2013.00467.15. Firestone A, Turk-Browne NB, Ryan JD.Age-related deficits in face recognition are relatedto underlying changes in scanning behavior. Aging,Neuropsychology, and Cognition 2007; 14(6):594-607.https://doi.org/10.1080/13825580600899717.

A Computational Model of Quantitatively Measuring the Alzheimer's Disease Progression in Face Identification

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A Computational Model of Quantitatively Measuring the Alzheimer's Disease Progression in Face Identification

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