Gas Discharge Visualization: An Imaging and Modeling Tool for Medical Biometrics

The need for automated identification of a disease makes the issue of medical biometrics very current in our society. Not all biometric tools available provide real-time feedback. We introduce gas discharge visualization (GDV) technique as one of the biometric tools that have the potential to identify deviations from the normal functional state at early stages and in real time. GDV is a nonintrusive technique to capture the physiological and psychoemotional status of a person and the functional status of different organs and organ systems through the electrophotonic emissions of fingertips placed on the surface of an impulse analyzer. This paper first introduces biometrics and its different types and then specifically focuses on medical biometrics and the potential applications of GDV in medical biometrics. We also present our previous experience with GDV in the research regarding autism and the potential use of GDV in combination with computer science for the potential development of biological pattern/biomarker for different kinds of health abnormalities including cancer and mental diseases

Analysis of the image moments sensitivity for the application in pattern recognition problems

Momenti slike su numerički deskriptori koji sadrže informaciju o svojstvima invarijantnim na translaciju, rotaciju, promjenu skale i neke oblike distorzije, a njihova analiza je jedna od metoda koje se često koriste pri analizi slika i raspoznavanju uzoraka. U okviru ove radnje razvijeni su algoritmi za računanje geometrijskih, Legendreovih, Zernikeovih, Fourier – Mellinovih te tri tipa Fourier – Jacobijevih momenata, kao i iz njih definiranih invarijanti slike u programskom jeziku MatLab uz rješavanje inverznog problema rekonstrukcije početnog ulaza. Za sve tipove momenata osim najjednostavnijih geometrijskih definirani su vektori osjetljivosti na rotaciju i promjenu skale čije su komponente oni članovi skupa koji nose značajnije informacije o ulaznoj slici. Primjenom novih deskriptora na klasifikaciju rukom pisanih slova i identifikacijskih fotografija osoba pokazano je da je relevantna informacija o ulazu na taj način sačuvana, a njihov je izračun znatno brži i jednostavniji uz zadržanu sposobnost jednoznačnog raspoznavanja uzoraka. Korištenjem momenata slike i vektora osjetljivosti analizirani su znakovi s dvaju glagoljskih spomenika te utvrđeno postojanje mješavine znakova trokutastog i okruglog modela glagoljice. Metoda je primijenjena i na klasifikaciju tragova puzanja ličinki mutanata vinske mušice za potrebe proučavanja odgovora živčanog sustava na različite podražaje.Image moments are numerical descriptors invariant to translation, rotation, change of scale and some types of image distortion and their analysis is one of the most often used methods in image processing and pattern recognition. In this work, algorithms for calculation of geometric, Legendre, Zernike, Fourier – Mellin and three types of Fourier – Jacobi moments were implemented in MatLab. Hu's, affine and blur invariants were also obtained as well as inverse problem of input image reconstruction solved. For each type of image moments exept geometric ones the set of sensitivity vectors for rotation and scale were defined. Their components are those image moments which describe more important features of the input image. These new descriptors were applied for classification of handwritten letters and identifying personal photos. It was shown that the process of such descriptor calculation is much faster and simpler while preserving all the relevant information about input image. Using this method, the signs carved in two glagolitic inscriptions were analyzed and the mixture of triangular and round glagolitic letters found. The method was also applied to classification of the mutant fruit fly larvae crawling trails which is needed in studying responses of the nervous system to different stimuli

Analysis of the image moments sensitivity for the application in pattern recognition problems

Momenti slike su numerički deskriptori koji sadrže informaciju o svojstvima invarijantnim na translaciju, rotaciju, promjenu skale i neke oblike distorzije, a njihova analiza je jedna od metoda koje se često koriste pri analizi slika i raspoznavanju uzoraka. U okviru ove radnje razvijeni su algoritmi za računanje geometrijskih, Legendreovih, Zernikeovih, Fourier – Mellinovih te tri tipa Fourier – Jacobijevih momenata, kao i iz njih definiranih invarijanti slike u programskom jeziku MatLab uz rješavanje inverznog problema rekonstrukcije početnog ulaza. Za sve tipove momenata osim najjednostavnijih geometrijskih definirani su vektori osjetljivosti na rotaciju i promjenu skale čije su komponente oni članovi skupa koji nose značajnije informacije o ulaznoj slici. Primjenom novih deskriptora na klasifikaciju rukom pisanih slova i identifikacijskih fotografija osoba pokazano je da je relevantna informacija o ulazu na taj način sačuvana, a njihov je izračun znatno brži i jednostavniji uz zadržanu sposobnost jednoznačnog raspoznavanja uzoraka. Korištenjem momenata slike i vektora osjetljivosti analizirani su znakovi s dvaju glagoljskih spomenika te utvrđeno postojanje mješavine znakova trokutastog i okruglog modela glagoljice. Metoda je primijenjena i na klasifikaciju tragova puzanja ličinki mutanata vinske mušice za potrebe proučavanja odgovora živčanog sustava na različite podražaje.Image moments are numerical descriptors invariant to translation, rotation, change of scale and some types of image distortion and their analysis is one of the most often used methods in image processing and pattern recognition. In this work, algorithms for calculation of geometric, Legendre, Zernike, Fourier – Mellin and three types of Fourier – Jacobi moments were implemented in MatLab. Hu's, affine and blur invariants were also obtained as well as inverse problem of input image reconstruction solved. For each type of image moments exept geometric ones the set of sensitivity vectors for rotation and scale were defined. Their components are those image moments which describe more important features of the input image. These new descriptors were applied for classification of handwritten letters and identifying personal photos. It was shown that the process of such descriptor calculation is much faster and simpler while preserving all the relevant information about input image. Using this method, the signs carved in two glagolitic inscriptions were analyzed and the mixture of triangular and round glagolitic letters found. The method was also applied to classification of the mutant fruit fly larvae crawling trails which is needed in studying responses of the nervous system to different stimuli

顔表情自動認識における西洋人と東洋人の基本的表情の違いに対する分析

Facial Expression Recognition (FER) has been one of the main targets of the well-known Human Computer Interaction (HCI) research field. Recent developments on this topic have attained high recognition rates under controlled and “in-the-wild” environments overcoming some of the main problems attached to FER systems, such as illumination changes, individual differences, partial occlusion, and so on. However, to the best of the author’s knowledge, all of those proposals have taken for granted the cultural universality of basic facial expressions of emotion. This hypothesis recently has been questioned and in some degree refuted by certain part of the research community from the psychological viewpoint. In this dissertation, an analysis of the differences between Western-Caucasian (WSN) and East-Asian (ASN) prototypic facial expressions is presented in order to assess the cultural universality from an HCI viewpoint. In addition, a full automated FER system is proposed for this analysis. This system is based on hybrid features of specific facial regions of forehead, eyes-eyebrows, mouth and nose, which are described by Fourier coefficients calculated individually from appearance and geometric features. The proposal takes advantage of the static structure of individual faces to be finally classified by Support Vector Machines. The culture-specific analysis is composed by automatic facial expression recognition and visual analysis of facial expression images from different standard databases divided into two different cultural datasets. Additionally, a human study applied to 40 subjects from both ethnic races is presented as a baseline. Evaluation results aid in identifying culture-specific facial expression differences based on individual and combined facial regions. Finally, two possible solutions for solving these differences are proposed. The first one builds on an early ethnicity detection which is based on the extraction of color, shape and texture representative features from each culture. The second approach independently considers the culture-specific basic expressions for the final classification process. In summary, the main contributions of this dissertation are: 1) Qualitative and quantitative analysis of appearance and geometric feature differences between Western-Caucasian and East-Asian facial expressions. 2) A fully automated FER system based on facial region segmentation and hybrid features. 3) The prior considerations for working with multicultural databases on FER. 4) Two possible solutions for FER with multicultural environments. This dissertation is organized as follows. Chapter 1 introduced the motivation, objectives and contributions of this dissertation. Chapter 2 presented, in detail, the background of FER and reviewed the related works from the psychological viewpoint along with the proposals which work with multicultural databases for FER from HCI. Chapter 3 explained the proposed FER method based on facial region segmentation. The automatic segmentation is focused on four facial regions. This proposal is capable to recognize the six basic expression by using only one part of the face. Therefore, it is useful for dealing with the problem of partial occlusion. Finally a modal value approach is proposed for unifying the different results obtained by facial regions of the same face image. Chapter 4 described the proposed fully automated FER method based on Fourier coefficients of hybrid features. This method takes advantage of information extracted from pixel intensities (appearance features) and facial shapes (geometric features) of three different facial regions. Hence, it also overcomes the problem of partial occlusion. This proposal is based on a combination of Local Fourier Coefficients (LFC) and Facial Fourier Descriptors (FFD) of appearance and geometric information, respectively. In addition, this method takes into account the effect of the static structure of the faces by subtracting the neutral face from the expressive face at the feature extraction level. Chapter 5 introduced the proposed analysis of differences between Western-Caucasian (WSN) and East-Asian (ASN) basic facial expressions, it is composed by FER and visual analysis which are divided by appearance, geometric and hybrid features. The FER analysis is focused on in- and out-group performance as well as multicultural tests. The proposed human study which shows cultural differences in perceiving the basic facial expressions, is also described in this chapter. Finally, the two possible solutions for working with multicultural environments are detailed, which are based on an early ethnicity detection and the consideration of previously found culture-specific expressions, respectively. Chapter 6 drew the conclusion and the future works of this research.電気通信大学201