Features extraction for low-power face verification

Mobile communication devices now available on the market, such as so-called smartphones, are far more advanced than the first cellular phones that became very popular one decade ago. In addition to their historical purpose, namely enabling wireless vocal communications to be established nearly everywhere, they now provide most of the functionalities offered by computers. As such, they hold an ever-increasing amount of personal information and confidential data. However, the authentication method employed to prevent unauthorized access to the device is still based on the same PIN code mechanism, which is often set to an easy-to-guess combination of digits, or even altogether disabled. Stronger security can be achieved by resorting to biometrics, which verifies the identity of a person based on intrinsic physical or behavioral characteristics. Since most mobile phones are now equipped with an image sensor to provide digital camera functionality, biometric authentication based on the face modality is very interesting as it does not require a dedicated sensor, unlike e.g. fingerprint verification. Its perceived intrusiveness is furthermore very low, and it is generally well accepted by users. The deployment of face verification on mobile devices however requires overcoming two major challenges, which are the main issues addressed in this PhD thesis. Firstly, images acquired by a handheld device in an uncontrolled environment exhibit strong variations in illumination conditions. The extracted features on which biometric identification is based must therefore be robust to such perturbations. Secondly, the amount of energy available on battery-powered mobile devices is tightly constrained, calling for algorithms with low computational complexity, and for highly optimized implementations. So as to reduce the dependency on the illumination conditions, a low-complexity normalization technique for features extraction based on mathematical morphology is introduced in this thesis, and evaluated in conjunction with the Elastic Graph Matching (EGM) algorithm. Robustness to other perturbations, such as occlusions or geometric transformations, is also assessed and several improvements are proposed. In order to minimize the power consumption, the hardware architecture of a coprocessor dedicated to features extraction is proposed and described in VHDL. This component is designed to be integrated into a System-on-Chip (SoC) implementing the complete face verification process, including image acquisition, thereby enabling biometric face authentication to be performed entirely on the mobile device. Comparison of the proposed solution with state-of-the-art academic results and recently disclosed commercial products shows that the chosen approach is indeed much more efficient energy-wise

Comparison of gene expression profiles in core biopsies and corresponding surgical breast cancer samples

INTRODUCTION: Gene expression profiling has been successfully used to classify breast cancer into clinically distinct subtypes, and to predict the risk of recurrence and treatment response. The aim of this study was to investigate whether the gene expression profile (GEP) detected in a core biopsy (CB) is representative for the entire tumor, since CB is an important tool in breast cancer diagnosis. Moreover, we investigated whether performing CBs prior to the surgical excision could influence the GEP of the respective tumor. METHODS: We quantified the RNA expression of 60 relevant genes by quantitative real-time PCR in paired CBs and surgical specimens from 22 untreated primary breast cancer patients. Subsequently, expression data were compared with independent GEPs obtained from tumors of 317 patients without preceding CB. RESULTS: In 82% of the cases the GEP detected in the CB correlated very well with the corresponding profile in the surgical sample (r(s )≥ 0.95, p < 0.001). Gene-by-gene analysis revealed four genes significantly elevated in the surgical sample compared to the CB; these comprised genes mainly involved in inflammation and the wound repair process as well as in tumor invasion and metastasis. CONCLUSION: A GEP detected in a CB are representative for the entire tumor and is, therefore, of clinical relevance. The observed alterations of individual genes after performance of CB deserve attention since they might impact the clinical interpretation with respect to prognosis and therapy prediction of the GEP as detected in the surgical specimen following CB performance

DNA methylation-based classification of sinonasal tumors

The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent potentially previously misclassified adenoid cystic carcinomas. Our findings can aid in improving the diagnostic classification of sinonasal tumors and could help to change the current perception of SNUCs