Search Word(s) in Title, Keywords, Authors, and Abstract: Pattern Recognition

Age Invariant Face Recognition Based on DCT Feature Extraction and Kernel Fisher Analysis
Leila Boussaad, Mohamed Benmohammed and Redha Benzid
Page: 392~409, Vol. 12, No.3, 2016
10.3745/JIPS.02.0043

Keywords: Active Appearance Model, Age-Invariant, Face Recognition, Kernel Fisher Analysis, 2D-Discrete Cosine Transform
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The aim of this paper is to examine the effectiveness of combining three popular tools used in pattern recognition, which are the Active Appearance Model (AAM), the two-dimensional discrete cosine transform (2D-DCT), and Kernel Fisher Analysis (KFA), for face recognition across age variations. For this purpose, we first used AAM to generate an AAM-based face representation; then, we applied 2D-DCT to get the descriptor of the image; and finally, we used a multiclass KFA for dimension reduction. Classification was made through a K-nearest neighbor classifier, based on Euclidean distance. Our experimental results on face images, which were obtained from the publicly available FG-NET face database, showed that the proposed descriptor worked satisfactorily for both face identification and verification across age progression.


Adaptive Cross-Device Gait Recognition Using a Mobile Accelerometer
Thang Hoang, Thuc Nguyen, Chuyen Luong, Son Do and Deokjai Choi
Page: 333~348, Vol. 9, No.2, 2013
10.3745/JIPS.2013.9.2.333
Keywords: Gait Recognition, Mobile Security, Accelerometer, Pattern Recognition, Authentication, Identification, Signal Processing
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Mobile authentication/identification has grown into a priority issue nowadays because of its existing outdated mechanisms, such as PINs or passwords. In this paper, we introduce gait recognition by using a mobile accelerometer as not only effective but also as an implicit identification model. Unlike previous works, the gait recognition only performs well with a particular mobile specification (e.g., a fixed sampling rate). Our work focuses on constructing a unique adaptive mechanism that could be independently deployed with the specification of mobile devices. To do this, the impact of the sampling rate on the preprocessing steps, such as noise elimination, data segmentation, and feature extraction, is examined in depth. Moreover, the degrees of agreement between the gait features that were extracted from two different mobiles, including both the Average Error Rate (AER) and Intra-class Correlation Coefficients (ICC), are assessed to evaluate the possibility of constructing a device-independent mechanism. We achieved the classification accuracy approximately 91.33 ± 0.67 % for both devices, which showed that it is feasible and reliable to construct adaptive cross-device gait recognition on a mobile phone.


Fingerprint Detection Using Canny Filter and DWT, a New Approach
Md. Imdadul Islam, Nasima Begum, Mahbubul Alam and M. R. Amin
Page: 511~520, Vol. 6, No.4, 2010
10.3745/JIPS.2010.6.4.511
Keywords: Canny Filter, Color Inversion, Skewness, Kurtosis and Convolution
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This paper proposes two new methods to detect the fingerprints of different persons based on one-dimensional and two-dimensional discrete wavelet transformations (DWTs). Recent literature shows that fingerprint detection based on DWT requires less memory space compared to pattern recognition and moment-based image recognition techniques. In this study four statistical parameters - cross correlation co-efficient, skewness, kurtosis and convolution of the approximate coefficient of one-dimensional DWTs are used to evaluate the two methods involving fingerprints of the same person and those of different persons. Within the contexts of all statistical parameters in detection of fingerprints, our second method shows better results than that of the first method.