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- Resultados preliminares de deteção de imagens de pêssegos aplicando o método Faster R-CNNPublication . Assunção, Eduardo Timóteo; Gaspar, Pedro Dinis; Mesquita, Ricardo; Veiros, André; Proença, HugoO modelo Faster R-CNN tem grande potencial de aplicação na deteção de pêssegos e poderá vir a ser uma boa ferramenta para estimar a producão em pomares, ajudando no planeamento da colheita e do armazenamento da fruta
- Results from MICHE II - Mobile Iris CHallenge Evaluation IIPublication . Marsico, Maria; Nappi, Michele; Proença, H.Mobile biometrics technologies are nowadays the new frontier for secure use of data and services, and are considered particularly important due to the massive use of handheld devices in the entire world. Among the biometric traits with potential to be used in mobile settings, the iris/ocular region is a natural can- didate, even considering that further advances in the technology are required to meet the operational requirements of such ambitious environments. Aiming at promoting these advances, we organized the Mobile Iris Challenge Evaluation (MICHE)-I contest. This paper presents a comparison of the performance of the participant methods by various Figures of Merit (FoMs). A particular at- tention is devoted to the identification of the image covariates that are likely to cause a decrease in the performance levels of the compared algorithms. Among these factors, interoperability among different devices plays an important role. The methods (or parts of them) implemented by the analyzed approaches are classified into segmentation (S), which was the main target of MICHE-I, and recognition (R). The paper reports both the results observed for either S or R, and also for different recombinations (S+R) of such methods. Last but not least, we also present the results obtained by multi-classifier strategies.
- Visual Surveillance and Biometrics: Practices, Challenges, and PossibilitiesPublication . Bakshi, Sambit; Guo, Guodong; Proença, H.; Tistarelli, MassimoVisual surveillance is the latest paradigm for social security through machine intelligence. It includes the use of visual data captured by infrared sensors or visible-light cameras mounted in cars, corridors, traffic signals etc. Visual surveillance facilitates the classification of human behavior, crowd activity, and gesture analysis to achieve application-specific objectives
- Deep-PRWIS: Periocular Recognition Without the Iris and Sclera Using Deep Learning FrameworksPublication . Proença, H.; Neves, JoãoThis work is based on a disruptive hypothesisfor periocular biometrics: in visible-light data, the recognitionperformance is optimized when the components inside the ocularglobe (the iris and the sclera) are simply discarded, and therecogniser’s response is exclusively based in information fromthe surroundings of the eye. As major novelty, we describe aprocessing chain based on convolution neural networks (CNNs)that defines the regions-of-interest in the input data that should beprivileged in an implicit way, i.e., without masking out any areasin the learning/test samples. By using an ocular segmentationalgorithm exclusively in the learning data, we separate the ocularfrom the periocular parts. Then, we produce a large set of”multi-class” artificial samples, by interchanging the periocularand ocular parts from different subjects. These samples areused for data augmentation purposes and feed the learningphase of the CNN, always considering as label the ID of theperiocular part. This way, for every periocular region, the CNNreceives multiple samples of different ocular classes, forcing itto conclude that such regions should not be considered in itsresponse. During the test phase, samples are provided withoutany segmentation mask and the networknaturallydisregardsthe ocular components, which contributes for improvements inperformance. Our experiments were carried out in full versionsof two widely known data sets (UBIRIS.v2 and FRGC) and showthat the proposed method consistently advances the state-of-the-art performance in theclosed-worldsetting, reducing the EERsin about 82% (UBIRIS.v2) and 85% (FRGC) and improving theRank-1 over 41% (UBIRIS.v2) and 12% (FRGC).
- IRINA: Iris Recognition (even) in Inacurately Segmented DataPublication . Proença, H.; Neves, JoãoThe effectiveness of current iris recognition systems de-pends on the accurate segmentation and parameterisationof the iris boundaries, as failures at this point misalignthe coefficients of the biometric signatures. This paper de-scribesIRINA, an algorithm forIrisRecognition that is ro-bust againstINAccurately segmented samples, which makesit a good candidate to work in poor-quality data. The pro-cess is based in the concept of ”corresponding” patch be-tween pairs of images, that is used to estimate the posteriorprobabilities that patches regard the same biological region,even in case of segmentation errors and non-linear texturedeformations. Such information enables to infer a free-formdeformation field (2D registration vectors) between images,whose first and second-order statistics provide effective bio-metric discriminating power. Extensive experiments werecarried out in four datasets (CASIA-IrisV3-Lamp, CASIA-IrisV4-Lamp, CASIA-IrisV4-Thousand and WVU) and showthat IRINA not only achieves state-of-the-art performancein good quality data, but also handles effectively severe seg-mentation errors and large differences in pupillary dilation/ constriction.
- A Leopard Cannot Change Its Spots: Improving Face Recognition Using 3D-based CaricaturesPublication . Neves, João; Proença, H.Caricatures refer to a representation of aperson in which the distinctive features are deliberatelyexaggerated, with several studies showing that humansperform better at recognizing people from caricaturesthan using original images. Inspired by this observa-tion, this paper introduces the first fully automatedcaricature-based face recognition approach capable ofworking with data acquired in the wild. Our approachleverages the 3D face structure from a single 2D imageand compares it to a reference model for obtaininga compact representation of face features deviations.This descriptor is subsequently deformed using a ’mea-sure locally, weight globally’ strategy to resemble thecaricature drawing process. The deformed deviationsare incorporated in the 3D model using the Laplacianmesh deformation algorithm, and the 2D face cari-cature image is obtained by projecting the deformedmodel in the original camera-view. To demonstratethe advantages of caricature-based face recognition, wetrain the VGG-Face network from scratch using eitheroriginal face images (baseline) or caricatured images,and use these models for extracting face descriptorsfrom the LFW, IJB-A and MegaFace datasets. The ex-periments show an increase in the recognition accuracywhen using caricatures rather than original images.Moreover, our approach achieves competitive resultswith state-of-the-art face recognition methods, evenwithout explicitly tuning the network for any of theevaluation sets.
- Resultados preliminares de deteção de imagens de pêssegos aplicando o método Faster R-CNNPublication . Assunção, Eduardo Timóteo; Gaspar, Pedro Dinis; Mesquita, Ricardo; Veiros, André; Proença, H.A deteção de frutos é de fundamental importância em sistemas de estimação de produção. Neste trabalho, são apresentados os resultados preliminares da utilização do método de deteção de objetos Faster R-CNN na deteção de imagens de pêssegos. O estudo consiste na avaliação do desempenho do método em imagens RGB obtidas em ambiente real num pomar. Embora este método de deteção tenha sido aplicado noutros trabalhos com o objetivo de detetar frutos, ainda não foi utilizado na deteção de pêssegos. A cor, a sua distribuição na árvore e a clusterização são características intrínsecas aos pêssegos. Os resultados obtidos, ainda que preliminares, mostram um elevado potencial da utilização do método na deteção destes frutos. Todavia, os resultados também mostram a necessidade de melhoria no desempenho. Isso pode ser alcançado com o aumento na quantidade de imagens de treino e também por definir um melhor critério de anotação dos frutos oclusos.
- Peaches Detection Using a Deep Learning Technique - A Contribution to Yield Estimation, Resources Management, and Circular EconomyPublication . Assunção, Eduardo Timóteo; Gaspar, Pedro Dinis; Mesquita, Ricardo; Simões, Maria Paula; Ramos, António; Proença, H.; Inácio, Pedro R. M.Fruit detection is crucial for yield estimation and fruit picking system performance. Many state-of-the-art methods for fruit detection use convolutional neural networks (CNNs). This paper presents the results for peach detection by applying a faster R-CNN framework in images captured from an outdoor orchard. Although this method has been used in other studies to detect fruits, there is no research on peaches. Since the fruit colors, sizes, shapes, tree branches, fruit bunches, and distributions in trees are particular, the development of a fruit detection procedure is specific. The results show great potential in using this method to detect this type of fruit. A detection accuracy of 0.90 using the metric average precision (AP) was achieved for fruit detection. Precision agriculture applications, such as deep neural networks (DNNs), as proposed in this paper, can help to mitigate climate change, due to horticultural activities by accurate product prediction, leading to improved resource management (e.g., irrigation water, nutrients, herbicides, pesticides), and helping to reduce food loss and waste via improved agricultural activity scheduling.
- Preliminary results of peach detection in images applying convolutional neuronal networkPublication . Assunção, Eduardo Timóteo; Proença, H.; Veiros, André; Mesquita, Ricardo; Gaspar, Pedro DinisThe fruit detection part is very important for a good performance in a yield estimation system. This paper presents the preliminary results using the object detection Faster R-CNN method in the peaches images. The aim is evaluate the method performance in the detection of peach RGB images. Images acquired in an orchard were used. Although this method of object detection has been applied in other studies to detect fruits, according to the literature, it has not been used to detect peaches. The results, although preliminary, show a great potential of using the method to detect peach.
- Fusing Vantage Point Trees and Linear Discriminants for Fast Feature ClassificationPublication . Proença, H.; Neves, JoãoThis paper describes a classification strategy that can be regarded as amore general form of nearest-neighbor classification. It fuses the concepts ofnearestneighbor,linear discriminantandVantage-Pointtrees, yielding an efficient indexingdata structure and classification algorithm. In the learning phase, we define a set ofdisjoint subspaces of reduced complexity that can be separated by linear discrimi-nants, ending up with an ensemble of simple (weak) classifiers that work locally. Inclassification, the closest centroids to the query determine the set of classifiers con-sidered, which responses are weighted. The algorithm was experimentally validatedin datasets widely used in the field, attaining error rates that are favorably compara-ble to the state-of-the-art classification techniques. Lastly, the proposed solution hasa set of interesting properties for a broad range of applications: 1) it is determinis-tic; 2) it classifies in time approximately logarithmic with respect to the size of thelearning set, being far more efficient than nearest neighbor classification in terms ofcomputational cost; and 3) it keeps the generalization ability of simple models.