Human identification systems are commonly used for security issues. Most of them are based on ID card. However, using an ID card for identification may not be safe enough since people may not have any protection against the theft. Another solution to the identification problem is to use iris or fingerprints. However, systems based on the iris or fingerprints need close interaction to identification machine. Identifying someone from his photograph overcomes all these problems which can be called as face recognition. Common face recognition systems are based on the 2D image recognition but success rates of these methods are strictly depending on the environment. Variations on brightness and pose, complex background are the main problems for 2D image recognition systems. At this point, three dimensional face recognition techniques gain importance. Although there are a lot of methods developed for 3D face recognition, many of them assume that face is not rotated and there is not any destructive (i.e. beard, moustache, hair, hat, and eyeglasses) on the face. However, identification needs to be done though these destructives. Basic step for the face recognition is the determination of the anchor points (i.e. nose tip, inner eye points). In this study, the goal is to implement previously proposed four face recognition methods based on anchor point detection / &ldquo / Multimodal Facial Feature Extraction for Automatic 3D Face Recognition&rdquo / , &ldquo / Automatic Feature Extraction for Multiview 3D Face Recognition&rdquo / , &ldquo / Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression&rdquo / , &ldquo / 3D face detection using curvature analysis&rdquo / , to compare the success rates of them for rotated and destructed images and finally to propose improvements on these methods.

Syfte: Denna uppsats behandlar en undermedveten kognitiv bias vilken benämns som ”anchoring effect” eller förankringseffekten. Effekten uppenbaras då människor tenderar att lägga för mycket tillit till den första informationen som görs tillgänglig vid olika typer av beslutsfattande. Teoriramen för detta forskningsområde är tämligen utbredd med drygt 40 år av studier som på senare tid börjat undersöka förankringseffektens påverkan vid förhandlingar. Dessutom finns utbredda konstateranden för att betydande kunskap inte lindrar effekten i någon större omfattning. Däremot föreligger bristande forskningsslutsatser kring hur kunskapsvarians vid förhandlingar  påverkar förankringseffekten. Till följd av detta avser studien att undersöka nedanstående syfte: Syftet med denna studie är att undersöka hur förankringseffekten påverkar utfallet i en förhandlingssituation, när kunskapsvarians råder mellan parterna gällande det aktuella förhandlingsområdet. Metod: I studien genomfördes ett experiment med totalt 44 deltaganden. Experimenten utgjordes av prisförhandlingar gällande en fiktiv bostad, där varje enskild deltagare fick genomgå två förhandlingar vardera. Den första förhandlingen avsåg en lägenhetsförsäljning och den andra en villaförsäljning där parterna agerade säljare respektive köpare. I experimentgruppen förelåg det kunskapsvarians då tredjeårsstudenter från fastighetsmäklarprogrammet mötte studenter med annan utbildningsbakgrund. I kontrollgruppen ställdes motsatsvis deltagare från samma utbildning mot varandra för att skapa mindre skillnader i kunskap beträffande det aktuella förhandlingsområdet. Resultat & slutsats: Resultatet i denna studie tyder på att deltagare vars kunskap stod dem till förfogande, alstrat förmånligare överenskommelser i jämförelse med deltagare med låg kunskapsnivå. Detta trots att deltagarna vars kunskapsnivå var låg, erhållit fördelen av förankringseffekten då de fick lägga det första budet. Resultatet indikerar därmed att förankringseffekten kan lindras till följd av kunskapsvarians vid förhandlingar. Förslag till vidare forskning: Vidare forskning bör utgå från liknande förhandlingsexperiment där forskaren i första hand eftersträvar att generera större kunskapsskillnader mellan parterna i förhandlingen. En större omfattning av denna studie torde således resultera i ökade statistiska klarheter vilket torde vara gynnsamt för det aktuella forskningsområdet. Uppsatsens bidrag: Studiens bidrag är att forskningsresultaten tyder på en lindring av förankringseffekten vid kunskapsvarians inom förhandlingar. Detta till skillnad från tidigare studier där olika kunskapnivåer inte visats ha någon större betydelse. I och med att ingen tidigare studie undersökt detta förhållande har denna studie lyckats identifiera ett tydligt forskningsgap som bidragits till. / Aim: This paper is about a subconscious cognitive bias referred to as "Anchoring Effect". The effect is revealed by the fact that people tend to put too much trust in the first information that is made available in different types of decision-making situations. The theory framework for this research area is fairly widespread with over 40 years of studies, and lately the research has begun examining the anchoring effect in different types of negotiation dyads. In addition, there are widespread findings that significant knowledge does not mitigate the effect in any notable degree. However, there is a lack of research findings regarding how differences in knowledge within negotiations might affect the anchoring effect. Therefore, this study intends to investigate the following: The purpose of this study is to investigate how the anchoring effect is affecting the outcome of a negotiation, when the parties have different levels of knowledge regarding the negotiated area. Method: This study has conducted an experiment with a total of 44 participants. The experiments have involved simulated price negotiations regarding a condominium and a residence property. Each participant performed two negotiations each, one for respective dwelling place. In the experimental group, there was a difference in knowledge when third year students from a real estate brokering program negotiated with students from other programs. In contradistinction to the experimental group, the control group included students with similar education background in order to create minor knowledge differences within the negotiated area. Result & Conclusion: The result of this study shows undeniably that the experiment participants with greater knowledge have generated more favourable agreements, compared to the participants with lower relevant knowledge. Even though the participants with lower knowledge had the advantage of presenting the initial offer in the experimental group. Thus, a mitigation of the anchoring effect has been identified as a result of differences in knowledge within the negotiations. Further research: Further research should be based on similar negotiation experiments with focus on creating greater differences in knowledge between the participants. This in combination with a larger replica of our study should enable increased statistical clarities with fruitful outcomes in this research field. Contribution of the thesis: The theoretical contribution of our study is primarily the fact that the anchoring effect tends to be mitigated by variance in knowledge within negotiations. Considering that no previous studies have examined this before, we argue that a clear research gap have been identified and that our findings has contributed to the theoretical framework.

<p> Trajectory planning is a field with widespread utility, and imitation learning pipelines<br> show promise as an accessible training method for trajectory planning. MPNet is the state<br> of the art for imitation learning with respect to success rates. MPNet has two general<br> components to its runtime: a neural network predicts the location of the next anchor point in<br> a trajectory, and then planning infrastructure applies sampling-based techniques to produce<br> near-optimal, collision-less paths. This distinction between the two parts of MPNet prompts<br> investigation into the role of the neural architectures in the Neural Motion Planning pipeline,<br> to discover where improvements can be made. This thesis seeks to explore the importance<br> of neural architecture choice by removing the planning structures, and comparing MPNet’s<br> feedforward anchor point predictor with that of a continuous model trained to output a<br> continuous trajectory from start to goal. A new state of the art model in continuous learning<br> is the Neural Flow model. As a continuous model, it possess a low standard deviation runtime<br> which can be properly leveraged in the absence of planning infrastructure. Neural Flows also<br> output smooth, continuous trajectory curves that serve to reduce noisy path outputs in the<br> absence of lazy vertex contraction. This project analyzes the performance of MPNet, Resnet<br> Flow, and Coupling Flow models when sampling-based planning tools such as dropout, lazy<br> vertex contraction, and replanning are removed. Each neural planner is trained end-to-end in<br> an imitation learning pipeline utilizing a simple feedforward encoder, a CNN-based encoder,<br> and a Pointnet encoder to encode the environment, for purposes of comparison. Results<br> indicate that performance is competitive, with Neural Flows slightly outperforming MPNet’s<br> success rates on our reduced dataset in Simple2D, and being slighty outperformed by MPNet<br> with respect to collision penetration distance in our UR5 Cubby test suite. These results<br> indicate that continuous models can compete with the performance of anchor point predictor<br> models when sampling-based planning techniques are not applied. Neural Flow models also<br> have other benefits that anchor point predictors do not, like continuity guarantees, the ability<br> to select a proportional location in a trajectory to output, and smoothness. </p>