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Different views of the face (a) original face (b) YCbCr representation of the face (c) Y component (d) Cb component ( e) Cr component
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Today, much research on face recognition has focused on using grey-scale images. With the increasing availability of color images, it makes sense to develop approaches for integrating color information into recognition process as the grey-scale approaches is sensitive to lighting variations. In this paper, we have proposed a novel two phase method,...
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Ear based identity recognition subject to uncontrolled conditions such as illumination changes, pose variation, low contrast, partial occlusion and noise, is an active research area in the field of biometrics. Meanwhile, multimodal biometrics is becoming increasingly popular and offers improved performance due to the use of multiple sources of info...
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... They were straight away implemented and tested their algorithms using standard face databases such as ORL (Our Research Laboratory) [9], Yale [10], FERET (Face Recognition Technology) [11,12] and other standard databases [13,14] developed in the year 1992-2015. Even the recent approaches DFBCS (Duplicating Facial images Based on Correlation Study) [15], PCA and WPD (Wavelet Packet Decomposition) using YCbCr colour space [16] and BPN (Back Propagation Neural Network) based multi classifier [17] have not discussed about these two problems. In our previous work [18,19], we analyzed the problems and errors arise in the development of FRT using Scilab software. ...
In traditional appearance based face recognition approaches, the facial images in standard face databases are used as it is for testing standard statistical face recognition algorithms. Due to the camera angle, position and lightening conditions, the facial image captured contains occlusion. Human pose, facial expressions and alignment of face with respect to camera axis also result in occluded image. Sometimes the background of the face also covered during the facial image capturing. We proposed an approach, called wavelet based facial image decomposition (WBFD) and in this approach the facial images are decomposed using two levels wavelet transform. In the faces obtained after two level wavelet decomposition, the occlusion and background images are suppressed. The face images from Yale database are used for our experiments. In order to test the performance of proposed WBFD approach, the face images obtained from WBFD approach, resized Yale face and Yale face from the original Yale face database are tested with five standard statistical appearance based face recognition algorithms such as eigen face, Fischer discriminant analysis, kernel principal component analysis, independent component analysis and 2D principal component analysis. The experimental results obtained show that, the proposed WBFD approach gives high recognition accuracy and require minimum recognition time in seconds for all the five face recognition methods.
... Intrapersonal variations in face images become more prominent than interpersonal variations due to this susceptibility. The effects of illumination deviations in face images are alleviated by various face recognition techniques [8][9][10][11][12][13][14][15][16][17][18][19][20][22][23][24][25]31,32], which broadly categorize these techniques as follows: ...
... Illumination-invariant feature extraction for FR using various transform domain techniques such as discrete cosine transform (DCT) [18,19], discrete wavelet transform (DWT) [20,22], double density dual tree complex wavelet transform (DD-DTCWT) [23], dual tree complex wavelet transform (DTCWT) [24], contourlet and curvelet transform [25] and discrete wavelet packet transform (DWPT) [31,32] falls under third category. One of the advantages of feature extraction using transform domain approaches is that large-scale and small-scale features in different directions can be analyzed for illumination-invariant FR. ...
... Discrete wavelet packet transform-based illumination robust face recognition techniques [31,32] are also popular under this category. In other applications also, such as medical volume segmentation in three dimension [26,27,29,30], DWPT allows multiresolution analysis. ...
Invariant feature extraction under diverse illuminations is challenging for face recognition. Light intensity variations in face images are predominant in large-scale features. In existing techniques, such features are truncated to achieve illumination-invariant features. However, salient features are lost during small-scale feature extraction that affect performance. Thus, objective in proposed work is to attain improved illumination normalization where large-scale and small-scale feature information is efficiently extracted for face recognition. First, reflectance ratio and histogram equalization (RRHE)-based new illumination normalization framework is proposed in which illumination deviations are annulled. Then, histogram equalization is employed to enhance contrast of reflectance ratio image by adjusting pixel intensities. Then, robust feature extraction in discrete wavelet packet transform domain (RFDWPT) from RRHE images is performed using various orthogonal wavelets with distinct vanishing moments. Here, small-scale features (noise effect) of RRHE images are discarded and final feature vector is formed by appropriate small-scale and large-scale features. This results in illumination-normalized salient features for eigen space analysis where nearest neighbor classification is performed on compact size training and test features. Compared with other face recognition techniques under different illumination conditions, significant enhancement in recognition accuracy is achieved on benchmark databases such as Yale B, CMU-PIE, Yale and extended Yale B.
... The first method used to describe face in our work is the principal component analysis method (Turk and Pentland, 1991;Sable and Talbar, 2016;Vinay et al., 2015), which is a classical method that has been widely used for human face representation and recognition. Its principle consists to find a lower dimensional space in which shorter length generated vectors will describe face images. ...
Face recognition is one of the important fields of search in computer
vision. Its principle consists to look for images that represent the similar faces
to a given face image the image request. This process is done by extracting a
set of features of the request image then making comparison between features
generated by the request one and the others extracted from whole face image
database. Recently, numerous face representation and classification methods
have been proposed in the literature. Nevertheless, many issues related to
indexing, combination of adequate descriptors and time computing have not yet
been solved. In this paper, we deal with problems related to features
combination and this, by conceiving a preformat content-based image retrieval
that is mainly oriented to handle face authentication challenges. Its convivial
interface allows to user the selection of appropriate weighting coefficient
values associate to each feature based on human judgment in order to enhance
the retrieval performance. We have tested our proposed method on ORL
database by using a set of known features. The obtained results show the
performance of our proposed method.
... With the development of image analysis, pattern recognition, neural network and cognitive science, as their interdiscipline, face recognition has also gotten numerous researches and applications. Nowadays, there exist many representative approaches for face recognition, such as Eigenface method [10], Fisherface method [11], Principle Component Analysis (PCA) [12], Independent Component Analysis (ICA) [13], Linear Discriminant Analysis (LDA) [14], Non-negative Matrix Factorization (NMF) [15,16], etc. ...
Face recognition has great theory research and application value. It is a very complicated problem which often suffers from variations in lighting condition, facial expression, head pose, glasses, background, and so on. This paper realizes an effective recognition for 108 face images of 27 individuals in complex background, through a biological inspired emergent developmental network (DN). To decrease the influence of complex background on the recognition of the foreground object, another biological inspired mechanism—synapse maintenance, which can dynamically determine which synapse should be removed, weaken or strengthened, is introduced to enhance the image recognition rate. To prevent the quick decay of the learning rate with the increasement of the neuron age, simulating the learning principle of the human brain, a new learning rate is proposed to determine the neuron learning process. Moreover, to exploit the network resource efficiently, neuron regenesis mechanism is designed to regulate the neuron resource dynamically. First, we design two kinds of neuron states to depict the neuron action, then, simulating the work mechanism of the human brain to produce new neurons continuously to learn new knowledge, we design the neuron regenesis mechanism to activate the suppressed old neurons in the developmental network to regenerate and learn new feature, thus to enhance the network usage efficiency. In order to demonstrate the effect of DN on face recognition, we compare and analyze the performances of DN with/without synapse maintenance mechanism, with the neuron regenesis mechanism. Experiment results in semi-constrained dataset and unconstrained dataset illustrate that DN with the synapse maintenance and neuron regenesis mechanism can effectively improve the face recognition rate in complex background. Further, comparing the performance of DN with some state-of-the-art algorithms, experimental results demonstrate the superior performance of the proposed method.
... Intrapersonal variations in face images become more prominent than interpersonal variations due to this susceptibility. The effects of illumination deviations in face images are alleviated by various face recognition techniques [8][9][10][11][12][13][14][15][16][17][18][19][20][22][23][24][25]31,32], which broadly categorize these techniques as follows: ...
... Illumination-invariant feature extraction for FR using various transform domain techniques such as discrete cosine transform (DCT) [18,19], discrete wavelet transform (DWT) [20,22], double density dual tree complex wavelet transform (DD-DTCWT) [23], dual tree complex wavelet transform (DTCWT) [24], contourlet and curvelet transform [25] and discrete wavelet packet transform (DWPT) [31,32] falls under third category. One of the advantages of feature extraction using transform domain approaches is that large-scale and small-scale features in different directions can be analyzed for illumination-invariant FR. ...
... Discrete wavelet packet transform-based illumination robust face recognition techniques [31,32] are also popular under this category. In other applications also, such as medical volume segmentation in three dimension [26,27,29,30], DWPT allows multiresolution analysis. ...
Invariant feature extraction under diverse illuminations is challenging for face recognition. Related face recognition techniques consider that illumination effect is predominant in low frequencies and involve various methods to segregate high frequency information. However, high frequency feature extraction results in loss of salient features that degrades performance. Thus, objective of this work is to extract illumination normalized robust facial features for face recognition under high illumination conditions. First, a new illumination normalization framework is proposed in which homomorphic filtering (HF) is applied for reducing illumination effect along with contrast enhancement and intensity range compression in face images. Then, illumination deviations are annulled by using reflectance ratio (RR), which yields appropriate texture smoothing and edge preservation. Further, selective feature extraction by discrete wavelet transform (DWT) is performed on HF and RR based face images that discards noise effect. It outcomes in illumination normalized significant facial features, on which subspace analysis (Principal component analysis) is performed to generate small size feature vectors for classification (k-nearest neighbour classifier). Experimental results on benchmark databases such as CMU-PIE, Yale B and Extended Yale B database, demonstrates that proposed face recognition technique yields high performance under diverse illuminations as compared to existing techniques.
... A.H. Sable, and S.N. Talbar, [17] evaluated a new illuminant FR methodology, a combination of both PCA and LDA methods for feature extraction. After extracting the feature information, the Mahalanobis Distance was computed between the testing and training facial images. ...
... Research results conclude that the YCbCr provide better segmentation results. Sable and Talbar [8] using the YCbCr color space to perform face detection. YCbCr components are processed with Wavelet Packet Decomposition (WPD), PCA and Mahalanobis. ...
... The rate of recognition gained by 90.50%, 95.87% and 98.99% respectively at three, five, and ten samples of each class. Some previous research, the utility of color space components for segmentation by thresholding method can be conducted, but the work is applied in cases such as skin detection [9], complicated image [7], and face detection [8]. So in this study the authors apply it in the case of mango leaf image. ...
Image Processing has taken a leap in the field of image processing with numbers increasing in the biometric technology. Face Recognition is one of the pioneering systems in the field of imaging and biometry. Though the algorithms have worked towards building up a fool proof system of recognition, there are outcomes which are not in line with the functional requirements due to sensitivity of the work not reaching required standards. This proposes use of ANFIS with PCA component using ORL and YALE B datasets. Dual Tree Complex Wavelet Transform approach has been used to obtain enhancement of facial image. The results show accuracy and sensitivity of 97.368 and 95 percent respectively. With other performance metrics like specificity, precision, recall Fscore and time were also verified to give result which is bringing face recognition close to reality for utilizing this for security based biometric technology.
Penelitian ini menyajikan kombinasi unik dari teknik ekstraksi fitur dan metode pengenalan yang bekerja baik pada lebih dari satu dataset wajah standar. Fokus utama dalam penelitian ini adalah bagaimana mendapatkan fitur dari setiap citra wajah untuk membedakan wajah satu sama lain dengan menerapkan metode Principal Component Analysis (PCA) sebagai ekstraksi ciri, dan Minimum Distance Classifier sebagai algoritma pengenalan sehingga didapatkan hasil pengenalan yang akurat. Untuk mencapai tujuan tersebut, perlu dilakukan studi kepustakaan untuk memahami konsep dan landasan teori agar dapat memperkuat asumsi metode Principal Component Analysis dan Minimum Distance Classifier. Hasil pengenalan dengan menggunakan data uji dari ORL database mendapatkan keakuratan sebesar 97%, sedangkan hasil pengenalan menggunakan data uji dari YALE database mendapatkan keakuratan sebesar 94,6%. Sehingga dapat disimpulkan bahwa penggabungan metode PCA dan Minimum Distance Classifier dapat memberikan solusi yang cepat dan sederhana dengan meningkatkan atau tanpa mengurangi akurasi standar.
