The digital image processing has been developed for a long time. The image detection and tracking are involved to a variety of digital techniques. In this research we introduce the digital image processing techniques, base on a boosted cascade of simple features to develop a face detection and tracking system. Due to a large amount of computation in face detection under the complex environment will affect the detection rate and velocity efficiency. Therefore, we use the extended feature and set of 45ﾟ rotated feature with fast feature computation which called the integral image, combine with the deformable templates. We can compute a part of the image block to reduce the computation and improve the system. In the PAN-TILT unit, we use fuzzy logic. The results of experiment show that system is robust and fast.

Face detection has many applications in technical area. We think about accuracy and regular arrangement of data of face detection. So, we select Recognition algorithms using neural network for implementation. The implementation method can be divided into three parts. One is Modified Census Transform. The other one is computing hypotheses. Other is square frame for mark face. Modified Census Transform is a regularly computing method and regular arrangement of data. Modified Census Transform is compatible using SIMD execution, but other parts is irregular arrangement of data and not easy to parallel execution. This paper uses SIMD processor architecture which develops in our laboratory to implementation of Modified Census Transform and multi-data streaming property. The picture is divided four parts to execute at the same time and changes different mode to execute according to different algorithm then fetch data is smooth and moving data can reduce frequency. Adding a new instruction that uses 16bits data format uses four MMX registers for 4¡Ñ4 transpose of the matrix. The other is loading data and extending signed bit or unsigned bit at the same time. They can accelerate parallel execution in multi-data streaming. We also support multi-data streaming that is not series. It uses striping mode to fetch multi-data which between the same distance then we can achieve to compute multi-data streaming. Besides, we use hypotheses to distinguish different person that we only want find one. We compare two hypotheses. If the difference in hypotheses between two different picture that there is small than 0.3%, they are the same person which in different picture. Finial, we verify the function is correct in UMVP-2500 platform. We compare efficiency with MMX and Xscale and analysis multi-data streaming SIMD architecture which has some benefits. We compare efficiency with MMX. We speed up 373%. We compare efficiency with Xscale. We speed up 345%. This result will show that multi-data streaming SIMD architecture compares speed up with others SIMD architecture. Multi-data streaming SIMD architecture adds a new instruction which is 4¡Ñ4 transpose of the matrix. Because the 4¡Ñ4 transpose of the matrix can change row and column, we have new abstraction. The common computation likes a line, but the new abstraction becomes a phase. MMX and Xscale are not this abstraction.

<p>Att applicera ansiktsigenkänning direkt på en mobiltelefon är en utmanande uppgift, inte minst med tanke på den begränsade minnes- och processorkapaciteten samt den stora variationen med avseende på ansiktsuttryck, hållning och ljusförhållande i inmatade bilder.</p><p>Det är fortfarande långt kvar till ett färdigutvecklat, robust och helautomatiskt ansiktsigenkänningssystem för den här miljön. Men resultaten i det här arbetet visar att genom att plocka ut feature-värden från lokala regioner samt applicera en välgjord warpstrategi för att minska problemen med variationer i position och rotation av huvudet, är det möjligt att uppnå rimliga och användbara igenkänningsnivåer. Speciellt för ett halvautomatiskt system där användaren har sista ordet om vem personen på bilden faktiskt är.</p><p>Med ett galleri bestående av 85 personer och endast en referensbild per person nådde systemet en igenkänningsgrad på 60% på en svårklassificerad serie testbilder. Totalt 73% av gångerna var den rätta individen inom de fyra främsta gissningarna.</p><p>Att lägga till extra referensbilder till galleriet höjer igenkänningsgraden rejält, till nästan 75% för helt korrekta gissningar och till 83,5% för topp fyra. Detta visar att en strategi där inmatade bilder läggs till som referensbilder i galleriet efterhand som de identifieras skulle löna sig ordentligt och göra systemet bättre efter hand likt en inlärningsprocess.</p><p>Detta exjobb belönades med pris för "Bästa industrirelevanta bidrag" vid Svenska sällskapet för automatiserad bildanalys årliga konferens i Lund, 13-14 mars 2008.</p> / <p>Applying face recognition directly on a mobile phone is a challenging proposal due to the unrestrained nature of input images and limitations in memory and processor capabilities.</p><p>A robust, fully automatic recognition system for this environment is still a far way off. However, results show that using local feature extraction and a warping scheme to reduce pose variation problems, it is possible to capitalize on high error tolerance and reach reasonable recognition rates, especially for a semi-automatic classification system where the user has the final say.</p><p>With a gallery of 85 individuals and only one gallery image per individual available the system is able to recognize close to 60 % of the faces in a very challenging test set, while the correct individual is in the top four guesses 73% of the time.</p><p>Adding extra reference images boosts performance to nearly 75% correct recognition and 83.5% in the top four guesses. This suggests a strategy where extra reference images are added one by one after correct classification, mimicking an online learning strategy.</p>

Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 77-82)f.

Thesis (Ph.D.)--Memorial University of Newfoundland, 2001. / Bibliography: leaves 210-216.

Thesis (Ph. D.)--University of Chicago, Dept. of Psychology, 2001. / Includes bibliographical references. Also available on the Internet.

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (leaves 88-92).

Thesis (Ph. D.)--University of California, San Diego, 2010. / Title from first page of PDF file (viewed Feb. 18, 2010). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 100-110).

As the demand for higher and more sophisticated security solutions has dramatically increased, a trustworthy and a more intelligent authentication technology has to takeover. That is biometric authentication. Although biometrics provides promising solutions, it is still a pattern recognition and artificial intelligence grand challenge. More importantly, biometric data in itself are vulnerable and requires comprehensive protection that ensures their security at every stage of the authentication procedure including the processing stage. Without this protection biometric authentication cannot replace traditional authentication methods. This protection however cannot be accomplished using conventional cryptographic methods due to the nature of biometric data, its usage and inherited dynamical changes. The new protection method has to transform the biometric data into a secure domain where original information cannot be reversed or retrieved. This secure domain has also to be suitable for accurate authentication performance. In addition, due to the permanence characteristic of the biometric data and the limited number of valid biometrics for each individual, the transform has to be able to generate multiple versions of the same original biometric trait. This to facilitate the replacement and the cancellation of any compromised transformed template with a newer one without compromising the security of the system. Hence the name of the transform that is best known as cancellable biometric. Two cancellable face biometric transforms have been designed, implemented and analysed in this thesis, the Polynomial and Co-occurrence Mapping (PCoM) and the Randomised Radon Signatures (RRS). The PCoM transform is based on high-order polynomial function mappings and co-occurrence matrices derived from the face images. The secure template is formed by the Hadamard product of the generated metrics. A mathematical framework of the two-dimensional Principal Component Analysis (2DPCA) recognition is established for accuracy performance evaluation and analysis. The RRS transform is based on the Radon Transform (RT) and the random projection. The Radon Signature is generated from the parametric Radon domain of the face and mixed with the random projection of the original face image. The transform relies on the extracted signatures and the Johnson-Lindenstrauss lemma for high accuracy performance. The Fisher Discriminant Analysis (FDA) is used for evaluating the accuracy performance of the transformed templates. Each of the transforms has its own security analysis besides a comprehensive security analysis for both. This comprehensive analysis is based on a conventional measure for the Exhaustive Search Attack (ESA) and a new derived measure based on the lower-bound guessing entropy for Smart Statistical Attack (SSA). This entropy measure is shown to be greater than the Shannon lower-bound of the guessing entropy for the transformed templates. This shows that the transforms provide greater security while the ESA analysis demonstrates immunity against brute force attacks. In terms of authentication performance, both transforms have either maintained or improved the accuracy of authentication. The PCoM has maintained the recognition rates for the CMU Advance Multimedia Processing Lab (AMP) and the CMU Pose, Illumination & Expression (PIE) databases at 98.35% and 90.13% respectively while improving the rate for the Olivetti Research Ltd (ORL) database to 97%. The transform has achieved a maximum recognition performance improvement of 4%. Meanwhile, the RRS transform has obtained an outstanding performance by achieving zero error rates for the ORL and PIE databases while improving the rate for the AMP by 37.50%. In addition, the transform has significantly enhanced the genuine and impostor distributions separations by 263.73%, 24.94% and 256.83% for the ORL, AMP and PIE databases while the overlap of these distributions have been completely eliminated for the ORL and PIE databases.

published_or_final_version / Psychology / Doctoral / Doctor of Philosophy